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In probability theory and statistics, the harmonic distribution is a continuous probability distribution. It was discovered by Étienne Halphen, who had become interested in the statistical modeling of natural events. His practical experience in data analysis motivated him to pioneer a new system of distributions that provided sufficient flexibility to fit a large variety of data sets. Halphen restricted his search to distributions whose parameters could be estimated using simple statistical approaches.

Is the harmonic distribution a discrete distribution?

No, it is a continuous distribution.

Khanuy-Gol (also known as Bulgan Volcanic Field, Chanuj Gol Hanui Gol or Hanuy Gol) is a volcanic field in Mongolia. Khanuy-Gol is located in the northern Hangai range, north of the settlement of Bulgan. Topographic map It covers a surface area of 3,500 square kilometres (1,400 sq mi) 150 kilometres (93 mi) southwest of Ulanbator. Among the 10 cones with heights of 30–190 metres (98–623 ft) that make up the field are Baga Togo Uul/Bogo-Togo-Ula (meaning "Little Togo Mountain", 28 metres (92 ft) high, 48°55.79′N 102°46.22′E to 48°55.34′N 102°45.75′E), Ikh Togo Uul/Ikha-Togo-Ula (meaning "Great Togo mountain", 219 metres (719 ft) high, 48°59.53′N 102°44.48′E), Togo, Urun Dush, and Uran Uul. This cone is the highest of the field, with a height of 280 metres (920 ft). Baga Togo Uul has one maar-like vent among its three vents, these vents are named Javalach, Togo and Tulga. Both Togo cones have a vegetation cover.

Where is the Bulgan Volcanic Field located?

The Bulgan Volcanic Field is located in Mongolia, in the northern Hangai range.

Where was the first publicly funded civil park?

The first publicly funded civil park is generally accepted to be Birkenhead Park, in England, United Kingdom.

For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. We trained an even stronger RoBERTa model by adding the training set from the second round (A2) to the training data. For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer.

What did the authors select to explore robustness under domain transfer for round 3?

A more diverse set of contexts.

Tell me whether these are animals or apache projects or both: Kudu, Impala, Alligator, Ant, Camel, Flink, Spark, Flume, Tomcat, Dog, Elephant

Animals: Alligator, Dog, Elephant Apache Projects: Flink, Spark, Flume Both animals and Apache projects: Kudu, Impala, Ant, Camel, Tomcat

Given the current banking crisis we are experiencing, is this a redux of the 2008 crisis?

While there are some similarities between 2023 and 2008, I do not believe this is a similar crisis. The biggest difference is that in 2008, nearly every bank had widespread exposure to mortgage backed securities. In the 2023 crisis, it is only a few banks that had overexposure to treasuries without any hedge against rising interest rates. Additionally, in 2008, while two different banks collapsed, there was much more widespread fear amongst the banks that caused a credit crunch. Banks were unwilling to lend each other money for fear of liquidity problems, which in turned led to the credit markets freezing and causing secondary problems of companies being unable to attain lines of credit to meet payroll. In 2023, there was also a failure of 2 banks, but the failure stemmed from a traditional bank run where depositors, en mass, were attempting to withdraw their funds at the same time, causing a liquidity problem with the affected banks.

In recent years, content delivery has changed drastically, shifting from offline methods to the Internet. It is now the primary source of information for a majority of the populace, especially for ever-changing news updates. This has also caused a shift in users' preferred sources. Previously, these preferences were static, sticking to a particular news source. Now, with the plethora of information available easily, there is no differentiation in the source it has been gathered from, with users opting to go for whatever is convenient. Keeping up with the times, news agencies have expanded their digital presence, increasing their reach exponentially. They generate revenue by (1) advertisements on their websites, or (2) a subscription based model for articles that might interest users. Since multiple agencies offer similar content, the user has his pick. To lure in more readers and increase the number of clicks on their content, subsequently enhancing their agency's revenue, writers have begun adopting a new technique - clickbait. Merriam-Webster defines clickbait as something (such as a headline) to encourage readers to click on hyperlinks based on snippets of information accompanying it, especially when those links lead to content of dubious value or interest. It is built to create, and consequently capitalise, on the Loewenstein information gap BIBREF0 by purposefully misrepresenting or promising what can be expected while reading a story on the web, be it through a headline, image or related text. We propose a two-pronged approach to detect such headlines. The first component leverages distributional semantics of the title text and models its temporal and sequential properties. The article title is represented as a concatenation of its sub-word level embeddings. The sub-word representation serves as input to a bidirectional LSTM network. The contribution of a sub-word towards the clickbait nature of the headline is calculated in a differential manner since the output of the LSTM is passed into an attention layer BIBREF1 , following which it goes through a dense layer. The second component focuses on Doc2Vec embeddings of the title and article content, performing an element wise multiplication of the two. This is concatenated with the dense layer output from the previous component. The obtained output is then passed through multiple hidden layers which performs the final classification. Previous work in this field that has exploited the power of embeddings has considered either word vectors, for their ability to create context-sensitive word representations, or character-level word embeddings to model the orthographic features of a word. We propose the use of sub-word level representations since it incorporates the word's morphological features. Attaching an attention mechanism to it helps us identify the surprise associated with each representation within the clickbait. One of the identifying characteristics of clickbait is that the article title differs from the text attached to it. For this reason, we define a component to capture the interaction between these attributes and augment our model. The first component leverages distributional semantics of the title text and models its temporal and sequential properties. The article title is represented as a concatenation of its sub-word level embeddings.

What is the article title represented as?

A concatenation of its sub-word level embeddings.

Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data. Here we outline the two types of neural networks under the same general framework: Various neural networks have been proposed for sentence pair modeling, all of which fall into two types of approaches. The sentence encoding approach encodes each sentence into a fixed-length vector and then computes sentence similarity directly. The model of this type has advantages in the simplicity of the network design and generalization to other NLP tasks. The sentence pair interaction approach takes word alignment and interactions between the sentence pair into account and often show better performance when trained on in-domain data.

What are the major types of the ordinary neural networks？

There are two types of approaches: one to encode each sentence into a fixed-length vector and then compute sentence similarity directly, and one to take word alignment and interactions between the sentence pair into account.

The Gulbenkian Prize for Humanity was given to whom?

Greta Thunberg

Schools and universities typically have a summer break to take advantage of the warmer weather and longer days. In almost all countries, children are out of school during this time of year for summer break, although dates vary. Many families will take holidays for a week or two over summer, particularly in Southern Hemisphere Western countries with statutory Christmas and New Year holidays. In the United States, public schools usually end in late May in Memorial Day weekend, while colleges finish in early May. Public school traditionally resumes near Labor Day, while higher institutions often resume in mid-August. In England and Wales, school ends in mid-July and resumes again in early September. In Scotland, the summer holiday begins in late June and ends in mid-to late-August. Similarly, in Canada the summer holiday starts on the last or second-last Friday in June and ends in late August or on the first Tuesday of September, with the exception of when that date falls before Labour Day, in which case, ends on the second Tuesday of the month. In Russia the summer holiday begins at the end of May and ends on 31 August. In the Southern Hemisphere, school summer holiday dates include the major holidays of Christmas and New Year's Day. School summer holidays in Australia, New Zealand and South Africa begin in early December and end in early February, with dates varying between states. In South Africa, the new school year usually starts during the second week of January, thus aligning the academic year with the Calendar year. In India, school ends in late April and resumes in early or mid-June. In Cameroon and Nigeria, schools usually finish for summer vacation in mid-July and resume in the later weeks of September or the first week of October.

Which holidays are statutory summer holidays in the southern hemisphere?

Southern Hemisphere countries have statutory Christmas and New Year holidays during the simmer break.

Let me know some different ways that someone can plan a trip

Here are some different ways you can plan a trip: - Research on forums such as TripAdvisor and discover how other people are planning trips - Talk to a travel agent in person - Look at organised package tours - Use a traveller aggregator website to explore travel routes - Go to a travel show and talk to different companies offering travel experiences

Think up of three different animals that lay eggs

Chickens, ducks and geese all lay eggs

What are some recommendations for US national parks to visit?

1. Yosemite National Park 2. Yellowstone National Park 3. Zion National Park 4. Bryce Canyon National Park 5. Glacier National Park

Unlike the conventional smooth nonconvex finite-sum problem where each mini-batch is iid-sampled from the dataset P ∪ C, the replay memory based continual learning encounters a non-iid stream of data C and has access to a small sized memory M . Algorithm 1 presents the pseudocode of NCCL by Equation. By the limited access to P , the gradient update for f (x) in Equation is a biased estimate of the gradient ∇f (x t ). Specifically, at the timestep t, where e t and e Mt denote the error term ∇f It (x t ) − ∇f (x t ) and the expectation value over I t given M t , respectively. We note that the given replay memory M t with small size at timestep t induces the inevitable overfitting bias. We first state an intermediate result for a single gradient update of NCCL. For ease of exposition, we define the overfitting term B t and the catastrophic forgetting term Γ t as follows: B t = (Lα 2 Ht − α Ht ) ∇f (x t ), e t + β Ht ∇g Jt (x t ), e t , where L is a constant for Lipschitz smoothness. Under Assumption 1, a single gradient update by Equation 7 satisfies the following bound by letting x ← x t+1 and y ← x t : This reveals the basic qualitative difference between the conventional nonconvex SGD and NCCL in the convergence rate. Compared to the nonconvex SGD, there exist two terms B t and Γ t in Equation. We group the terms containing e t as B t and the other terms as Γ t . We note that the catastrophic forgetting term Γ t has ∇f It (x t ), ∇g Jt (x t ) , which is the key aspect of interference and transer, and B t includes the error term between the batch of M and the entire dataset P . Then, we can quantify the amount of overfitting by tracing B t . To compute the expectation over the stochasticity of NCCL, we derive the expectation of ∇f Mt (x t ) over the sampling rule of M t . The episodic memory M t = M 0 for all t is uniformly sampled once from the random sequence of P , and ER-reservoir iteratively samples the replay memory M t by the selection probability If M 0 is uniformly sampled from P , then both episodic memory and ER-reservoir satisfies We provide the detailed proof in Appendix B. Note that taking expectation iteratively with respect to the history M [0:t] is needed to compute the expected value of gradients for M t . Since taking the expectation over the stochasticity of NCCL implies the total expectation Algorithm 1 Nonconvex Continual Learning (NCCL) Compute learning rates α Ht and β Ht with ∇f It (x t ) and ∇g Jt (x t ) Store J t into M t+1 by the rule of replay memory scheme end for is also 0. We note that considering a random choice of M 0 allows us to analyze the convergence on f (x), even if M 0 cannot access the all datapoints in P to compute ∇f (x t ) 2 . Our first main result is the following lemma that provides the stepwise change of upper bound. Lemma 2. Suppose that Assumption 1 holds and 0<α Ht ≤ 2 L . Then for x t updated by Algorithm 1, we have the following bound where the effect of B t vanishes by The proof is presented in Appendix B. Surprisingly, taking the expectation over M 0 ⊂ P in Equation to handle the stochasticity of choosing M 0 allows us to analyze the convergence of f (x) in Section 3.3. We also note that the individual trial with a randomly given M 0 cannot cancel the effect by B t , although its total expectation over the whole possible trials E[B t ] is zero. More specifically, the worst case sup M0 ∇f (x t ) 2 definitely contains the non-zero value of B t in the upper bound term. We discuss the more details of the overfitting to memory in Appendix D. This reveals the basic qualitative difference between the conventional nonconvex SGD and NCCL in the convergence rate. Compared to the nonconvex SGD, there exist two terms Bt and Γt in Equation 8. We group the terms containing et as Bt and the other terms as Γt. We note that the catastrophic forgetting term Γt has h∇fIt (x t ), ∇gJt (x t )i, which is the key aspect of interference and transer (Riemer et al., 2018), and Bt includes the error term between the batch of M and the entire dataset P. Then, we can quantify the amount of overfitting by tracing Bt.

Bt and Γt appears rather abuptly and can you explain them?

We added some explnation below the Equation 8 for readibility.

What is the main tax-related difference between a brokerage account and an IRA ?

The main difference tax-related difference between a brokerage account and an IRA is that the gains made on investments in a brokerage account are subject to capital gains taxes while gains in an IRA are not.

Who was the drummer for the band Rush?

Neil Peart was the longest standing drummer for Rush

Why can't the whole world be 1 country?

The whole world cannot be one country due to cultural and geographical differences. Different countries have different languages, customs, religions, and values. There is no one set of laws, values, or language that can be applied to all countries.

Classify each car as a Ford or Tesla: Model Y, F150, Focus, Fiesta, Model X

Model Y: Tesla F150: Ford Focus: Ford Fiesta: Ford Model X: Tesla

In recent years, the micro-blogging platform Twitter has become a major social media platform with hundreds of millions of users. People turn to Twitter for a variety of purposes, from everyday chatter to reading about breaking news. The volume plus the public nature of Twitter (less than 10% of Twitter accounts are private BIBREF0 ) have made Twitter a great source of data for social and behavioural studies. These studies often require an understanding of what people are tweeting about. Though this can be coded manually, in order to take advantage of the volume of tweets available automatic analytic methods have to be used. There has been extensive work done on computational methods for analysing the linguistic content of tweets. However, there has been very little work done on classifying the pragmatics of tweets. Pragmatics looks beyond the literal meaning of an utterance and considers how context and intention contribute to meaning. A major element of pragmatics is the intended communicative act of an utterance, or what the utterance was meant to achieve. It is essential to study pragmatics in any linguistic system because at the core of linguistic analysis is studying what language is used for or what we do with language. Linguistic communication and meaning can not truly be studied without studying pragmatics. Proposed by Austin BIBREF1 and refined by Searle BIBREF2 , speech act theory can be used to study pragmatics. Amongst other things, the theory provides a formalized taxonomy BIBREF3 of a set of communicative acts, more commonly known as speech acts. There has been extensive research done on speech act (also known as dialogue act) classification in computational linguistics, e.g., BIBREF4 . Unfortunately, these methods do not map well to Twitter, given the noisy and unconventional nature of the language used on the platform. In this work, we created a supervised speech act classifier for Twitter, using a manually annotated dataset of a few thousand tweets, in order to be better understand the meaning and intention behind tweets and uncover the rich interactions between the users of Twitter. Knowing the speech acts behind a tweet can help improve analysis of tweets and give us a better understanding of the state of mind of the users. Moreover, ws we have shown in our previous works BIBREF5 , BIBREF6 , speech act classification is essential for detection of rumors on Twitter. Finally, knowing the distribution of speech acts of tweets about a particular topic can reveal a lot about the general attitude of users about that topic (e.g., are they confused and are asking a lot of questions? Are they outraged and demanding action? Etc). There has been extensive research done on speech act (also known as dialogue act) classification in computational linguistics, e.g., (Stolcke et al. 2000). Unfortunately, these methods do not map well to Twitter, given the noisy and unconventional nature of the language used on the platform. In this work, we created a supervised speech act classifier for Twitter, using a manually annotated dataset of a few thousand tweets, in order to be better understand the meaning and intention behind tweets and uncover the rich interactions between the users of Twitter.

Is their classifier the first supervised speech act classifier for Twitter?

Yes.

1906.01512

Modules: Modules are the basic building blocks of different models. In LeafNATS, we provide ready-to-use modules for constructing recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS, e.g., pointer-generator network BIBREF1 . These modules include embedder, RNN encoder, attention BIBREF24 , temporal attention BIBREF6 , attention on decoder BIBREF2 and others. We also use these basic modules to assemble a pointer-generator decoder module and the corresponding beam search algorithms. The embedder can also be used to realize the embedding-weights sharing mechanism BIBREF2 . Modules: Modules are the basic building blocks of different models. In LeafNATS, we provide ready-to-use modules for constructing recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS, e.g., pointer-generator network BIBREF1 . These modules include embedder, RNN encoder, attention BIBREF24 , temporal attention BIBREF6 , attention on decoder BIBREF2 and others. We also use these basic modules to assemble a pointer-generator decoder module and the corresponding beam search algorithms. The embedder can also be used to realize the embedding-weights sharing mechanism BIBREF2 .

What models are included in the toolkit?

The answers are shown as follows: * recurrent neural network (RNN)-based sequence-to-sequence (Seq2Seq) models for NATS

Good Friday is a Christian holiday commemorating the crucifixion of Jesus and his death at Calvary. It is observed during Holy Week as part of the Paschal Triduum. It is also known as Holy Friday, Great Friday, Great and Holy Friday (also Holy and Great Friday), and Black Friday. Members of many Christian denominations, including the Catholic, Eastern Orthodox, Lutheran, Anglican, Methodist, Oriental Orthodox, United Protestant and some Reformed traditions (including certain Continental Reformed, Presbyterian and Congregationalist churches), observe Good Friday with fasting and church services. In many Catholic, Lutheran, Anglican and Methodist churches, the Service of the Great Three Hours' Agony is held from noon until 3 pm, the time duration that the Bible records as darkness covering the land to Jesus' sacrificial death on the cross. Communicants of the Moravian Church have a Good Friday tradition of cleaning gravestones in Moravian cemeteries. The date of Good Friday varies from one year to the next in both the Gregorian and Julian calendars. Eastern and Western Christianity disagree over the computation of the date of Easter and therefore of Good Friday. Good Friday is a widely instituted legal holiday around the world, including in most Western countries and 12 U.S. states. Some predominantly Christian countries, such as Germany, have laws prohibiting certain acts such as dancing and horse racing, in remembrance of the somber nature of Good Friday

What are other names used for Good Friday?

Other names used for Good Friday are: Black Friday, Great and Holy Friday, Holy and Great Friday, Holy Friday, and Great Friday.

What is "cycling" in ice hockey?

Standard ice hockey is a game with a goaltender and 5 skaters per side played on an ice surface divided with a center red line, two goal line red lines and two blue lines indicating the respective offensive zones. Cycling is when the offensive team has possession of the puck in their respective offensive zone and the skaters, typically three forwards, are passing the puck to each other, while moving in a circular motion, to create space and opportunity to generate a shot on goal. The movement and skating in this fashion is known as, "cycling" and is an offensive tactic employed in ice hockey leagues from youth to professional.

Give me three recipes from different cuisines that I can make with potatoes and chicken

You could make potato dumplings in a chicken broth, which comes from German cuisine. Another recipe is mashed potatoes and fried chicken from the American South. You could also try making a Thai curry with chicken and potatoes.

Tokyo (/ˈtoʊkioʊ/; Japanese: 東京, Tōkyō, [toːkʲoː] (listen)), officially the Tokyo Metropolis (東京都, Tōkyō-to), is the capital and most populous city of Japan. Formerly known as Edo, its metropolitan area (13,452 square kilometers or 5,194 square miles) is the most populous in the world, with an estimated 37.468 million residents as of 2018; the city proper has a population of 13.99 million people. Located at the head of Tokyo Bay, the prefecture forms part of the Kantō region on the central coast of Honshu, Japan's largest island. Tokyo serves as Japan's economic center and is the seat of both the Japanese government and the Emperor of Japan.

What is the current capital of Japan?

Tokyo is the current capital of Japan.

What is Fibonacci series

Fibonacci series is a sequence where every number is the sum of the 2 preceding numbers. The series commonly starts form 0 and the first few numbers are 0,1,1,2,3,5,8,13,21....

In 2001, Keshubhai Patel's health was failing and the BJP lost a few state assembly seats in by-elections. Allegations of abuse of power, corruption and poor administration were made, and Patel's standing had been damaged by his administration's handling of the earthquake in Bhuj in 2001. The BJP national leadership sought a new candidate for the chief ministership, and Modi, who had expressed misgivings about Patel's administration, was chosen as a replacement. Although Advani did not want to ostracise Patel and was concerned about Modi's lack of experience in government, Modi declined an offer to be Patel's deputy chief minister, telling Advani and Atal Bihari Vajpayee that he was "going to be fully responsible for Gujarat or not at all". On 3 October 2001 he replaced Patel as chief minister of Gujarat, with the responsibility of preparing the BJP for the December 2002 elections. On 7 October, he was sworn in, and entered the Gujarat state legislature on 24 February 2002 by winning a by-election to the Rajkot – II constituency, defeating Ashwin Mehta of the INC.

Why was Advani concerned about Modi’s appointment as a chief minister of Gujarat?

For Modi being a candidate for the chief minister of Gujarat, Advani was concerned about Modi’s lack of political experience.

Is it important to have friends?

Yes, it is important to have friends. Friends can provide camaraderie, support, guidance and encourage you to think in new and different ways by challenging your beliefs and assumptions. Many people have different types or categories of friends. For example, some friends may be individuals with whom specific activities are shared such as music or sports. Other friends may be individuals from whom you seek advice on complex personal decisions. Another category or type of friend may be one or more individuals with whom you can share almost anything without fear of embarrassment or being judged. It is particularly important to have an active social life as one gets older. Positive social interactions help older adults avoid loneliness and stay healthy.

Identify which instrument is string or percussion: Calung, Maguhu

Maguhu is string, Calung is percussion.

Give me five data themed cocktail drinks.

1 The Datatini - like a martini! 2. The Collaborative Collins - Like a Tom Collins but open sourced! 3. The Spicy Data-rita - A spicy take on a margarita with all the tequila you could want. 4. The ETL Long Island - Like all long islands, it's multi layered and nuanced. Like ETL, it uploads faster and cheaper. 5. The Large Language Models Mule - Very on-trend and always served cold.

Tell me whether each of these is red or brown: tomato, strawberry, raspberry, chocolate, coffee, almond

Tomato: Red Strawberry: Red Raspberry: Red Chocolate: Brown Coffee: Brown Almond: Brown

Cardiovascular diseases are the most common cause of death globally as of 2008, accounting for 30% of deaths. Of these more than three-quarters are a result of coronary artery disease and stroke. Risk factors include: smoking, being overweight, little exercise, high cholesterol, high blood pressure, and poorly controlled diabetes, among others. Cardiovascular diseases do not frequently have symptoms but may cause chest pain or shortness of breath. Diagnosis of heart disease is often done by the taking of a medical history, listening to the heart-sounds with a stethoscope, ECG, echocardiogram, and ultrasound. Specialists who focus on diseases of the heart are called cardiologists, although many specialties of medicine may be involved in treatment.

Given this paragraph about heart, for how many deaths (in %) as of 2008 do cardiovascular diseases account for?

30%

1909.06937

FLOAT SELECTED: Table 6: Results on our CAIS dataset, where “†” indicates our implementation of the S-LSTM. FLOAT SELECTED: Table 6: Results on our CAIS dataset, where “†” indicates our implementation of the S-LSTM.

What was the performance on the self-collected corpus?

F1 scores of 86.16 on slot filling and 94.56 on intent detection

Novartis AG is a Swiss multinational pharmaceutical corporation based in Basel, Switzerland and Cambridge, Massachusetts, United States (global research). Consistently ranked in the global top five, Novartis is one of the largest pharmaceutical companies in the world and was the fourth largest by revenue in 2022.

Who is the company Novartis

Novartis AG is a Swiss multinational pharmaceutical corporation based in Basel, Switzerland and Cambridge, Massachusetts, United States (global research). Consistently ranked in the global top five, Novartis is one of the largest pharmaceutical companies in the world and was the fourth largest by revenue in 2022.

Winemaking (also wine making) or vinification is the production of wine, starting with the selection of the fruit, its fermentation into alcohol, and the bottling of the finished liquid. The history of wine-making stretches over millennia. The science of wine and winemaking is known as oenology. A winemaker may also be called a vintner. The growing of grapes is viticulture and there are many varieties of grapes. Winemaking can be divided into two general categories: still wine production (without carbonation) and sparkling wine production (with carbonation – natural or injected). Red wine, white wine, and rosé are the other main categories. Although most wine is made from grapes, it may also be made from other plants. (See fruit wine.) Other similar light alcoholic drinks (as opposed to beer or spirits) include mead, made by fermenting honey and water, cider ("apple cider"), made by fermenting the juice of apples, and perry ("pear cider"), made by fermenting the juice of pears, and kumis, made of fermented mare's milk.

How do you make wine?

Winemaking is the process of using fruit for fermentation into alcohol. The science of winemaking is called enology. There are two general types of wine production: still wine and sparkling wine. The three categories of wine are red, white, and rose. Most wine is made from grapes but others can include apples, pears, and honey.

Classify each of the following as bright or dark: sun, lamp, cave, light, night, tunnel

Sun: Bright Lamp: Bright Cave: Dark Light: Bright Night: Dark Tunnel: Dark

Neural machine translation (NMT) exploits neural networks to directly learn to transform sentences from a source language to a target language BIBREF0 , BIBREF1 . Universal multilingual NMT discovered that a neural translation system can be trained on datasets containing source and target sentences in multiple languages BIBREF2 , BIBREF3 . Successfully trained models using this approach can be used to translate arbitrarily between any languages included in the training data. In low-resource scenarios, multilingual NMT has proven to be an extremely useful regularization method since each language direction benefits from the information of the others BIBREF4 , BIBREF5 . An important research focus of multilingual NMT is zero-shot translation (ZS), or translation between languages included in multilingual data for which no directly parallel training data exists. Application-wise, ZS offers a faster and more direct path between languages compared to pivot translation, which requires translation to one or many intermediate languages. This can result in large latency and error propagation, common issues in non-end-to-end pipelines.From a representation learning point of view, there is evidence of NMT's ability to capture language-independent features, which have proved useful for cross-lingual transfer learning BIBREF6 , BIBREF7 and provide motivation for ZS translation. However it is still unclear if minimizing the difference in representations between languages is beneficial for zero-shot learning. On the other hand, the current neural architecture and learning mechanisms of multilingual NMT is not geared towards having a common representation. Different languages are likely to convey the same semantic content with sentences of different lengths BIBREF8 , which makes the desiderata difficult to achieve. Moreover, the loss function of the neural translation model does not favour having sentences encoded in the same representation space regardless of the source language. As a result, if the network capacity is large enough, it may partition itself into different sub-spaces for different language pairs BIBREF9 . Our work here focuses on the zero-shot translation aspect of universal multilingual NMT. First, we attempt to investigate the relationship of encoder representation and ZS performance. By modifying the Transformer architecture of BIBREF10 to afford a fixed-size representation for the encoder output, we found that we can significantly improve zero-shot performance at the cost of a lower performance on the supervised language pairs. To the best of our knowledge, this is the first empirical evidence showing that the multilingual model can capture both language-independent and language-dependent features, and that the former can be prioritized during training. This observation leads us to the most important contribution in this work, which is to propose several techniques to learn a joint semantic space for different languages in multilingual models without any architectural modification. The key idea is to prefer a source language-independent representation in the decoder using an additional loss function. As a result, the NMT architecture remains untouched and the technique is scalable to the number of languages in the training data. The success of this method is shown by significant gains on zero-shot translation quality in the standard IWSLT 2017 multilingual benchmark BIBREF11 . Finally, we introduce a more challenging scenario that involves more than one bridge language between source and target languages. This challenging setup confirms the consistency of our zero-shot techniques while clarifying the disadvantages of pivot-based translation. To the best of our knowledge, this is the first empirical evidence showing that the multilingual model can capture both language-independent and language-dependent features, and that the former can be prioritized during training.

What features can the multilingual model capture?

Both language-independent and language-dependent features.

Tendulkar is cross-dominant: he bats, bowls and throws with his right hand, but writes with his left hand. He also practices left-handed throws at the nets on a regular basis. Cricinfo columnist Sambit Bal has described him as the "most wholesome batsman of his time". His batting is based on complete balance and poise while limiting unnecessary movements and flourishes. He appears to show little preference for the slow and low wickets which are typical in India, and has scored many centuries on the hard, bouncy pitches in South Africa and Australia. He is known for his unique punch style of hitting the ball over square. He is also renowned for his picture-perfect straight drive, often completed with no follow-through. The straight drive is often said to be his favourite shot. In 2008, Sunil Gavaskar, in an article he wrote in the AFP, remarked that "it is hard to imagine any player in the history of the game who combines classical technique with raw aggression like the little champion does". Equipment-wise, his bats tend to be heavier than the average batman.

Pleaese give me a short list of the main characteristics of cricketer Sachin Tendulkar's playing style.

Cross-dominant Batting with balance, unnecessary movements are limited Unique punch style of hitting the ball over square Textbook straight drive, often with no follow-through His bat is heavier than the average batsman's bat

Black Bear Road is open a few months of the year, from late summer (usually the last week of July) to early fall. The road is traveled only downhill from Red Mountain Pass — except for the annual Jeeper's Jamboree in which travel is reversed for one day only. The start of the trail was formerly marked along U.S. 550 with a sign that read:

Can someone drive on Black Bear Road all year?

No, it is only open for a few months of the year.

Why do I have a belly button?

When we were a baby we were connected to our mother through an umbilical cord that provided food, water and nutrients to help us grow. The belly button is the spot where the cord was once attached from.

Typical speech-to-text translation systems pipeline automatic speech recognition (ASR) and machine translation (MT) BIBREF0 . But high-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world's estimated 7,000 languages BIBREF1 . Nevertheless, there are important low-resource settings in which even limited speech translation would be of immense value: documentation of endangered languages, which often have no writing system BIBREF2 , BIBREF3 ; and crisis response, for which text applications have proven useful BIBREF4 , but only help literate populations. In these settings, target translations may be available. For example, ad hoc translations may be collected in support of relief operations. Can we do anything at all with this data? In this exploratory study, we present a speech-to-text translation system that learns directly from source audio and target text pairs, and does not require intermediate ASR or MT. Our work complements several lines of related recent work. For example, duong2015attentional and antonios+chiang+duongEMNLP2016 presented models that align audio to translated text, but neither used these models to try to translate new utterances (in fact, the latter model cannot make such predictions). berard+etalnipsworkshop16 did develop a direct speech to translation system, but presented results only on a corpus of synthetic audio with a small number of speakers. Finally, Adams et al. adams+etalinterspeech16,adams+etalemnlp16 targeted the same low-resource speech-to-translation task, but instead of working with audio, they started from word or phoneme lattices. In principle these could be produced in an unsupervised or minimally-supervised way, but in practice they used supervised ASR/phone recognition. Additionally, their evaluation focused on phone error rate rather than translation. In contrast to these approaches, our method can make translation predictions for audio input not seen during training, and we evaluate it on real multi-speaker speech data. Our simple system (§ SECREF2 ) builds on unsupervised speech processing BIBREF5 , BIBREF6 , BIBREF7 , and in particular on unsupervised term discovery (UTD), which creates hard clusters of repeated word-like units in raw speech BIBREF8 , BIBREF9 . The clusters do not account for all of the audio, but we can use them to simulate a partial, noisy transcription, or pseudotext, which we pair with translations to learn a bag-of-words translation model. We test our system on the CALLHOME Spanish-English speech translation corpus BIBREF10 , a noisy multi-speaker corpus of telephone calls in a variety of Spanish dialects (§ SECREF3 ). Using the Spanish speech as the source and English text translations as the target, we identify several challenges in the use of UTD, including low coverage of audio and difficulty in cross-speaker clustering (§ SECREF4 ). Despite these difficulties, we demonstrate that the system learns to translate some content words (§ SECREF5 ). We test our system on the CALLHOME Spanish-English speech translation corpus, a noisy multi-speaker corpus of telephone calls in a variety of Spanish dialects (§ SECREF3 ).

How do the authors test their system?

Test their system on the CALLHOME Spanish-English speech translation corpus.

We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants. doc2vec BIBREF37. Doc2vec (or paragraph vector) is an extension of word2vec that learns vectors for documents in a fully unsupervised manner. Document embeddings are then fed to a logistic regression classifier. CNN BIBREF38. The convolutional neural network architecture, well-known in computer vision, is applied to text. There is one spatial dimension and the word embeddings are used as channels (depth dimensions). DAN BIBREF39. The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax. Tree-LSTM BIBREF40 is a generalization of the standard LSTM architecture to constituency and dependency parse trees. DRNN BIBREF41. Recursive neural networks are stacked and applied to parse trees. LSTMN BIBREF42 is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations. C-LSTM BIBREF43 combines convolutional and recurrent neural networks. The region embeddings provided by a CNN are fed to a LSTM. SPGK BIBREF44 also models documents as word co-occurrence networks. It computes a graph kernel that compares shortest paths extracted from the word co-occurrence networks and then uses a SVM to categorize documents. WMD BIBREF45 is an application of the well-known Earth Mover's Distance to text. A k-nearest neighbor classifier is used. S-WMD BIBREF46 is a supervised extension of the Word Mover's Distance. Semantic-CNN BIBREF47. Here, a CNN is applied to semantic units obtained by clustering words in the embedding space. LSTM-GRNN BIBREF26 is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector. HN-ATT BIBREF27 is another hierarchical model, where the same encoder architecture (a bidirectional GRU-RNN) is used for both sentences and documents, with different parameters. A self-attention mechanism is applied to the RNN annotations at each level. We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants. Doc2vec (Le and Mikolov 2014) is an extension of word2vec that learns vectors for documents in a fully un_x001f_supervised manner. Document embeddings are then fed to a logistic regression classifier. CNN (Kim 2014). 1D convolutional neural network where the word embeddings are used as channels (depth dimensions). DAN (Iyyer et al. 2015). The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax. Tree-LSTM (Tai, Socher, and Manning 2015) is a generalization of the standard LSTM architecture to constituency and dependency parse trees. DRNN (Irsoy and Cardie 2014). Recursive neural networks are stacked and applied to parse trees. LSTMN (Cheng, Dong, and Lapata 2016) is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations. C-LSTM (Zhou et al. 2015) combines convolutional and recurrent neural networks. The region embeddings provided by a CNN are fed to a LSTM. SPGK (Nikolentzos et al. 2017) also models documents as word co-occurrence networks. It computes a graph kernel that compares shortest paths extracted from the word cooccurrence networks and then relies on a SVM. WMD (Kusner et al. 2015) is an application of the well- known Earth Mover's Distance to text. A k-nearest neighbor classifier is used. DiSAN (Shen et al. 2018) uses directional self-attention along with multi-dimensional attention to generate document representations. LSTM-GRNN (Tang, Qin, and Liu 2015) is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector. HN-ATT (Yang et al. 2016) is another hierarchical model, where the same encoder architecture (a bidirectional GRU- RNN) is used for both sentences and documents. Selfattention is applied at each level.

What baseline models are MPAD compared with

The baselines are: Doc2vec (Le and Mikolov 2014) ; CNN (Kim 2014);DAN (Iyyer et al. 2015).; Tree-LSTM (Tai, Socher, and Manning 2015);DRNN (Irsoy and Cardie 2014); LSTMN (Cheng, Dong, and Lapata 2016) ; C-LSTM (Zhou et al. 2015); SPGK (Nikolentzos et al. 2017); WMD (Kusner et al. 2015); DiSAN (Shen et al. 2018);LSTM-GRNN (Tang, Qin, and Liu 2015) and HN-ATT (Yang et al. 2016).

Think of five quirky marketing slogans for a new Fashion Brand TV campaign. It must be a little arrogant and sharp.

1. "There is no good and evil. There is only [BRAND] and those too weak to see it." 2. "For the advanced mind, [BRAND] is but the next great adventure. 3. "Earth, Wind, Water & Fire is for present. [BRAND] is for future. 4. "[BRAND]. Style for the ages in an ageless dystopian world. 5. The show must go on. The show is.. [BRAND].

Who are the founders of OpenAI?

OpenAI was founded by by Sam Altman, Reid Hoffman, Ilya Sutskever, Elon Musk, Wojciech Zaremba, Peter Thiel, Jessica Livingston and others in 2015.

1910.04601

We deployed the task on Amazon Mechanical Turk (AMT). To see how reasoning varies across workers, we hire 3 crowdworkers per one instance. We hire reliable crowdworkers with $\ge 5,000$ HITs experiences and an approval rate of $\ge $ 99.0%, and pay ¢20 as a reward per instance. We deployed the task on Amazon Mechanical Turk (AMT)

Did they use any crowdsourcing platform?

Yes.

What is a zamboni?

A zamboni is a machine that resurfaces ice rinks used for skating. They are used in recreational skating, professional skating, team skating, and even on olympic skating rinks. After the ice is resurfaced skating becomes easier and more safe because there is less risk for a trip hazard on the ice.

Arshdeep Singh (born 7 May 1994) is an Indian professional footballer who plays as a forward for Minerva Punjab F.C. in the I-League.

What sport does Arshdeep play?

football

Name some of the best Taylor Swift songs

Anti-Hero, You Belong With Me, Blank Space, All Too Well (10 Minute Version), Champagne Problems, Love Story

We posses a wealth of prior knowledge about many natural language processing tasks. For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification. A key problem arisen here, is how to leverage such knowledge to guide the learning process, an interesting problem for both NLP and machine learning communities. Previous studies addressing the problem fall into several lines. First, to leverage prior knowledge to label data BIBREF1 , BIBREF2 . Second, to encode prior knowledge with a prior on parameters, which can be commonly seen in many Bayesian approaches BIBREF3 , BIBREF4 . Third, to formalise prior knowledge with additional variables and dependencies BIBREF5 . Last, to use prior knowledge to control the distributions over latent output variables BIBREF6 , BIBREF7 , BIBREF8 , which makes the output variables easily interpretable. However, a crucial problem, which has rarely been addressed, is the bias in the prior knowledge that we supply to the learning model. Would the model be robust or sensitive to the prior knowledge? Or, which kind of knowledge is appropriate for the task? Let's see an example: we may be a baseball fan but unfamiliar with hockey so that we can provide a few number of feature words of baseball, but much less of hockey for a baseball-hockey classification task. Such prior knowledge may mislead the model with heavy bias to baseball. If the model cannot handle this situation appropriately, the performance may be undesirable. In this paper, we investigate into the problem in the framework of Generalized Expectation Criteria BIBREF7 . The study aims to reveal the factors of reducing the sensibility of the prior knowledge and therefore to make the model more robust and practical. To this end, we introduce auxiliary regularization terms in which our prior knowledge is formalized as distribution over output variables. Recall the example just mentioned, though we do not have enough knowledge to provide features for class hockey, it is easy for us to provide some neutral words, namely words that are not strong indicators of any class, like player here. As one of the factors revealed in this paper, supplying neutral feature words can boost the performance remarkably, making the model more robust. More attractively, we do not need manual annotation to label these neutral feature words in our proposed approach. More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution. For the first manner, we simply use the most common features as neutral features and assume the neutral features are distributed uniformly over class labels. For the second and third one, we assume we have some knowledge about the class distribution which will be detailed soon later. To summarize, the main contributions of this work are as follows: The rest of the paper is structured as follows: In Section 2, we briefly describe the generalized expectation criteria and present the proposed regularization terms. In Section 3, we conduct extensive experiments to justify the proposed methods. We survey related work in Section 4, and summarize our work in Section 5. More specifically, we explore three regularization terms to address the problem:

How many auxiliary regularization terms do the authors introduce?

Three regularization terms.

Model Flow++ only Short-run EBM CoopFlow FID ↓ 92.10 49.51 21.16 Table 6: FID score of normalizing flow and short-run EBM on CIFAR-10 dataset.

Shall the baseline in Table 1 also include the result of Ho2019 (used in the proposed model) in the category of Flow, to see if the MCMC teaching is important?

The original paper of Ho2019 doesn't report their FID score. And we have done the ablation study of using our own implementation of the normalizing flow structure of Ho2019 (this implemetation has also been used as a part of our CoopFlow). The results are shown in Table 6 in the appendix. You can see the FID score we got is 92.10 for the normalizing flow only case, while the score for CoopFlow (even without pretraining the normalizing flow model) can reach 21.16. We don't include this results in Table 1 because we are not sure whether using the offcial implementation of Tensorflow might achieve a better result (we can not find a pretrained checkpoint in their website). But given that we have use the same implementation (and model strucuture) in both the normalizing flow only case, i.e. flow++(Ho2019) as well as the CoopFlow case, we believe it is fair to draw the conclusion that the Langevin Flow (i.e., MCMC teaching) is impoatant because it can improve the performance of the normalizing fow. Also, you can easily find the gap between flow++(Ho2019) and our CoopFlow by simply looking at the synthesized images on cifar10 provided by these two models. Comparing our Figure 4(a) and Ho2019's Figure 2(b) (or Ho2019's Figure 6 in the supplement), we can easily find that our generated samples are much better.

1910.12129

The main contribution of our work is thus a new parallel data-to-text NLG corpus that (1) is more conversational, rather than information seeking or question answering, and thus more suitable for an open-domain dialogue system, (2) represents a new, unexplored domain which, however, has excellent potential for application in conversational agents, and (3) has high-quality, manually cleaned human-produced utterances. The main contribution of our work is thus a new parallel data-to-text NLG corpus that (1) is more conversational, rather than information seeking or question answering, and thus more suitable for an open-domain dialogue system, (2) represents a new, unexplored domain which, however, has excellent potential for application in conversational agents, and (3) has high-quality, manually cleaned human-produced utterances.

How the authors made sure that corpus is clean despite being crowdsourced?

The answers are shown as follows: * manually cleaned human-produced utterances

1808.03986

Exemplars aim to provide appropriate context. To better understand the context, we experimented by analysing the questions generated through an exemplar. We observed that indeed a supporting exemplar could identify relevant tags (cows in Figure FIGREF3 ) for generating questions. We improve use of exemplars by using a triplet network. This network ensures that the joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption and vice-versa. We empirically evaluated whether an explicit approach that uses the differential set of tags as a one-hot encoding improves the question generation, or the implicit embedding obtained based on the triplet network. We observed that the implicit multimodal differential network empirically provided better context for generating questions. Our understanding of this phenomenon is that both target and supporting exemplars generate similar questions whereas contrasting exemplars generate very different questions from the target question. The triplet network that enhances the joint embedding thus aids to improve the generation of target question. These are observed to be better than the explicitly obtained context tags as can be seen in Figure FIGREF2 . We now explain our method in detail. Exemplars aim to provide appropriate context. To better understand the context, we experimented by analysing the questions generated through an exemplar. We observed that indeed a supporting exemplar could identify relevant tags (cows in Figure FIGREF3 ) for generating questions. We improve use of exemplars by using a triplet network. This network ensures that the joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption and vice-versa.

How do the authors define exemplars?

The answers are shown as follows: * Exemplars aim to provide appropriate context. * joint image-caption embedding for the supporting exemplar are closer to that of the target image-caption

In the following experiment, we focus on automatic identification of arguments in the discourse. Our approach is based on supervised and semi-supervised machine learning methods on the gold data Toulmin dataset introduced in section SECREF51 . An argument consists of different components (such as premises, backing, etc.) which are implicitly linked to the claim. In principle one document can contain multiple independent arguments. However, only 4% of the documents in our dataset contain arguments for both sides of the issue. Thus we simplify the task and assume there is only one argument per document. Given the low inter-annotator agreement on the pathos dimension (Table TABREF77 ), we focus solely on recognizing the logical dimension of argument. The pathos dimension of argument remains an open problem for a proper modeling as well as its later recognition. Since the smallest annotation unit is a token and the argument components do not overlap, we approach identification of argument components as a sequence labeling problem. We use the BIO encoding, so each token belongs to one of the following 11 classes: O (not a part of any argument component), Backing-B, Backing-I, Claim-B, Claim-I, Premise-B, Premise-I, Rebuttal-B, Rebuttal-I, Refutation-B, Refutation-I. This is the minimal encoding that is able to distinguish two adjacent argument components of the same type. In our data, 48% of all adjacent argument components of the same type are direct neighbors (there are no "O" tokens in between). We report Macro- INLINEFORM0 score and INLINEFORM1 scores for each of the 11 classes as the main evaluation metric. This evaluation is performed on the token level, and for each token the predicted label must exactly match the gold data label (classification of tokens into 11 classes). As instances for the sequence labeling model, we chose sentences rather than tokens. During our initial experiments, we observed that building a sequence labeling model for recognizing argument components as sequences of tokens is too fine-grained, as a single token does not convey enough information that could be encoded as features for a machine learner. However, as discussed in section UID73 , the annotations were performed on data pre-segmented to sentences and annotating tokens was necessary only when the sentence segmentation was wrong or one sentence contained multiple argument components. Our corpus consists of 3899 sentences, from which 2214 sentences (57%) contain no argument component. From the remaining ones, only 50 sentences (1%) have more than one argument component. Although in 19 cases (0.5%) the sentence contains a Claim-Premise pair which is an important distinction from the argumentation perspective, given the overall small number of such occurrences, we simplify the task by treating each sentence as if it has either one argument component or none. The approximation with sentence-level units is explained in the example in Figure FIGREF112 . In order to evaluate the expected performance loss using this approximation, we used an oracle that always predicts the correct label for the unit (sentence) and evaluated it against the true labels (recall that the evaluation against the true gold labels is done always on token level). We lose only about 10% of macro INLINEFORM0 score (0.906) and only about 2% of accuracy (0.984). This performance is still acceptable, while allowing to model sequences where the minimal unit is a sentence. Table TABREF114 shows the distribution of the classes in the gold data Toulmin, where the labeling was already mapped to the sentences. The little presence of rebuttal and refutation (4 classes account only for 3.4% of the data) makes this dataset very unbalanced. We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. Each sentence ( INLINEFORM0 ) is represented as a vector of real-valued features. We defined the following feature sets: FS0: Baseline lexical features word uni-, bi-, and tri-grams (binary) FS1: Structural, morphological, and syntactic features First and last 3 tokens. Motivation: these tokens may contain discourse markers or other indicators for argument components, such as “therefore” and “since” for premises or “think” and “believe” for claims. Relative position in paragraph and relative position in document. Motivation: We expect that claims are more likely to appear at the beginning or at the end of the document. Number of POS 1-3 grams, dependency tree depth, constituency tree production rules, and number of sub-clauses. Based on BIBREF113 . FS2: Topic and sentiment features 30 features taken from a vector representation of the sentence obtained by using Gibbs sampling on LDA model BIBREF114 , BIBREF115 with 30 topics trained on unlabeled data from the raw corpus. Motivation: Topic representation of a sentence might be valuable for detecting off-topic sentences, namely non-argument components. Scores for five sentiment categories (from very negative to very positive) obtained from Stanford sentiment analyzer BIBREF116 . Motivation: Claims usually express opinions and carry sentiment. FS3: Semantic, coreference, and discourse features Binary features from Clear NLP Semantic Role Labeler BIBREF117 . Namely, we extract agent, predicate + agent, predicate + agent + patient + (optional) negation, argument type + argument value, and discourse marker which are based on PropBank semantic role labels. Motivation: Exploit the semantics of Capturing the semantics of the sentences. Binary features from Stanford Coreference Chain Resolver BIBREF118 , e.g., presence of the sentence in a chain, transition type (i.e., nominal–pronominal), distance to previous/next sentences in the chain, or number of inter-sentence coreference links. Motivation: Presence of coreference chains indicates links outside the sentence and thus may be informative, for example, for classifying whether the sentence is a part of a larger argument component. Results of a PTDB-style discourse parser BIBREF119 , namely the type of discourse relation (explicit, implicit), presence of discourse connectives, and attributions. Motivation: It has been claimed that discourse relations play a role in argumentation mining BIBREF120 . FS4: Embedding features 300 features from word embedding vectors using word embeddings trained on part of the Google News dataset BIBREF121 . In particular, we sum up embedding vectors (dimensionality 300) of each word, resulting into a single vector for the entire sentence. This vector is then directly used as a feature vector. Motivation: Embeddings helped to achieve state-of-the-art results in various NLP tasks BIBREF116 , BIBREF122 . Except the baseline lexical features, all feature types are extracted not only for the current sentence INLINEFORM0 , but also for INLINEFORM1 preceding and subsequent sentences, namely INLINEFORM2 , INLINEFORM3 , INLINEFORM4 INLINEFORM5 , INLINEFORM6 , where INLINEFORM7 was empirically set to 4. Each feature is then represented with a prefix to determine its relative position to the current sequence unit. Let us first discuss the upper bounds of the system. Performance of the three human annotators is shown in the first column of Table TABREF139 (results are obtained from a cumulative confusion matrix). The overall Macro- INLINEFORM0 score is 0.602 (accuracy 0.754). If we look closer at the different argument components, we observe that humans are good at predicting claims, premises, backing and non-argumentative text (about 0.60-0.80 INLINEFORM1 ), but on rebuttal and refutation they achieve rather low scores. Without these two components, the overall human Macro- INLINEFORM2 would be 0.707. This trend follows the inter-annotator agreement scores, as discussed in section UID75 . In our experiments, the feature sets were combined in the bottom-up manner, starting with the simple lexical features (FS0), adding structural and syntactic features (FS1), then adding topic and sentiment features (FS2), then features reflecting the discourse structure (FS3), and finally enriched with completely unsupervised latent vector space representation (FS4). In addition, we were gradually removing the simple features (e.g., without lexical features, without syntactic features, etc.) to test the system with more “abstract” feature sets (feature ablation). The results are shown in Table TABREF139 . The overall best performance (Macro- INLINEFORM0 0.251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets. Classification of non-argumentative text (the "O" class) yields about 0.7 INLINEFORM1 score even in the baseline setting. The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I). It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder. The very low numbers for rebuttal and refutation have two reasons. First, these two argument components caused many disagreements in the annotations, as discussed in section UID86 , and were hard to recognize for the humans too. Second, these four classes have very few instances in the corpus (about 3.4%, see Table TABREF114 ), so the classifier suffers from the lack of training data. The results for the in-domain cross validation scenario are shown in Table TABREF140 . Similarly to the cross-validation scenario, the overall best results were achieved using the largest feature set (01234). For mainstreaming and red-shirting, the best results were achieved using only the feature set 4 (embeddings). These two domains contain also fewer documents, compared to other domains (refer to Table TABREF71 ). We suspect that embeddings-based features convey important information when not enough in-domain data are available. This observation will become apparent in the next experiment. The cross-domain experiments yield rather poor results for most of the feature combinations (Table TABREF141 ). However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario. These results indicate that embedding features generalize well across domains in our task of argument component identification. We leave investigating better performing vector representations, such as paragraph vectors BIBREF123 , for future work. Error analysis based on the probabilistic confusion matrix BIBREF124 shown in Table TABREF142 reveals further details. About a half of the instances for each class are misclassified as non-argumentative (the "O" prediction). Backing-B is often confused with Premise-B (12%) and Backing-I with Premise-I (23%). Similarly, Premise-I is misclassified as Backing-I in 9%. This shows that distinguishing between backing and premises is not easy because these two components are similar such that they support the claim, as discussed in section UID86 . We can also see that the misclassification is consistent among *-B and *-I tags. Rebuttal is often misclassified as Premise (28% for Rebuttal-I and 18% for Rebuttal-B; notice again the consistency in *-B and *-I tags). This is rather surprising, as one would expect that rebuttal would be confused with a claim, because its role is to provide an opposing view. Refutation-B and Refutation-I is misclassified as Premise-I in 19% and 27%, respectively. This finding confirms the discussion in section UID86 , because the role of refutation is highly context-dependent. In a pragmatic perspective, it is put forward to indirectly support the claim by attacking the rebuttal, thus having a similar function to the premise. We manually examined miss-classified examples produced the best-performing system to find out which phenomena pose biggest challenges. Properly detecting boundaries of argument components caused problems, as shown in Figure FIGREF146 (a). This goes in line with the granularity annotation difficulties discussed in section UID86 . The next example in Figure FIGREF146 (b) shows that even if boundaries of components were detected precisely, the distinction between premise and backing fails. The example also shows that in some cases, labeling on clause level is required (left-hand side claim and premise) but the approximation in the system cannot cope with this level of detail (as explained in section UID111 ). Confusing non-argumentative text and argument components by the system is sometimes plausible, as is the case of the last rhetorical question in Figure FIGREF146 (c). On the other hand, the last example in Figure FIGREF146 (d) shows that some claims using figurative language were hard to be identified. The complete predictions along with the gold data are publicly available. SVMhmm offers many hyper-parameters with suggested default values, from which three are of importance. Parameter INLINEFORM0 sets the order of dependencies of transitions in HMM, parameter INLINEFORM1 sets the order of dependencies of emissions in HMM, and parameter INLINEFORM2 represents a trading-off slack versus magnitude of the weight-vector. For all experiments, we set all the hyper-parameters to their default values ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 ). Using the best performing feature set from Table TABREF139 , we experimented with a grid search over different values ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) but the results did not outperform the system trained with default parameter values. The INLINEFORM0 scores might seem very low at the first glance. One obvious reason is the actual performance of the system, which gives a plenty of room for improvement in the future. But the main cause of low INLINEFORM2 numbers is the evaluation measure — using 11 classes on the token level is very strict, as it penalizes a mismatch in argument component boundaries the same way as a wrongly predicted argument component type. Therefore we also report two another evaluation metrics that help to put our results into a context. Krippendorff's INLINEFORM0 — It was also used for evaluating inter-annotator agreement (see section UID75 ). Boundary similarity BIBREF125 — Using this metric, the problem is treated solely as a segmentation task without recognizing the argument component types. As shown in Table TABREF157 (the Macro- INLINEFORM0 scores are repeated from Table TABREF139 ), the best-performing system achieves 0.30 score using Krippendorf's INLINEFORM1 , which is in the middle between the baseline and the human performance (0.48) but is considered as poor from the inter-annotator agreement point of view BIBREF54 . The boundary similarity metrics is not directly suitable for evaluating argument component classification, but reveals a sub-task of finding the component boundaries. The best system achieved 0.32 on this measure. Vovk2013MT used this measure to annotate argument spans and his annotators achieved 0.36 boundary similarity score. Human annotators in BIBREF125 reached 0.53 boundary similarity score. The overall performance of the system is also affected by the accuracy of individual NPL tools used for extracting features. One particular problem is that the preprocessing models we rely on (POS, syntax, semantic roles, coreference, discourse; see section UID115 ) were trained on newswire corpora, so one has to expect performance drop when applied on user-generated content. This is however a well-known issue in NLP BIBREF126 , BIBREF127 , BIBREF128 . To get an impression of the actual performance of the system on the data, we also provide the complete output of our best performing system in one PDF document together with the gold annotations in the logos dimension side by side in the accompanying software package. We believe this will help the community to see the strengths of our model as well as possible limitations of our current approaches. These results indicate that embedding features generalize well across domains in our task of argument component identification.

Do the embedding features generalize well across domains in the task of argument component identification?

Yes, they do.

Provide a bulleted list of ways to spend less money

The following are ways to spend less money 1. Make a budget and track spending 2. Cook your own food 3. Don't drink alcohol 4. Delete the Amazon application 5. Have get togethers at home 6. Find a cheaper place to live 7. Negotiate insurance rates

What is the best location for an all-season vacation home with proximity to Philadelphia?

There are many options with lakes, mountains and beaches within 150 miles of Philadelphia. Rehoboth Beach, Dewey Beach and Bethany Beach Delaware are accessible and family-friendly destinations that have something for everyone; with beautiful parks, free beaches and tax-free outlet shopping. There is also a vibrant restaurant scene with top-notch seafood and highly-rated breweries that remain open year-round.

Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an"apparently positive"sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.

What generalizability issue did they address?

Classifying data that have not been seen by the models at learning phase.

Multilingual language models is not a new idea BIBREF3 , the novelty of our work lies primarily in the use of language vectors and the empirical evaluation using nearly a thousand languages. Concurrent with this work, Johnson2016zeroshot conducted a study using neural machine translation (NMT), where a sub-word decoder is told which language to generate by means of a special language identifier token in the source sentence. This is close to our model, although beyond a simple interpolation experiment (as in our sec:generating) they did not further explore the language vectors, which would have been challenging to do given the small number of languages used in their study. Ammar2016manylanguages used one-hot language identifiers as input to a multilingual word-based dependency parser, based on multilingual word embeddings. Given that they report this resulting in higher accuracy than using features from a typological database, it is a reasonable guess that their system learned language vectors which were able to encode syntactic properties relevant to the task. Unfortunately, they also did not look closer at the language vector space, which would have been interesting given the relatively large and diverse sample of languages represented in the Universal Dependencies treebanks. Our evaluation in sec:clustering calls to mind previous work on automatic language classification, by Wichmann2010evaluating among others. However, our purpose is not to detect genealogical relationships, even though we use the strong correlation between such classifications and our language vectors as evidence that the vector space captures sensible information about languages. Multilingual language models is not a new idea (Fugen et al., 2003), the novelty of our work lies primarily in the use of language vectors and the empirical evaluation using nearly a thousand languages.

What is the novelty of their work?

The use of language vectors and the empirical evaluation using nearly a thousand languages.

We build our bilingual LMs, named RAMEN, starting from BERT$_{\textsc {base}}$, BERT$_{\textsc {large}}$, RoBERTa$_{\textsc {base}}$, and RoBERTa$_{\textsc {large}}$ pre-trained models. Using BERT$_{\textsc {base}}$ allows us to compare the results with mBERT model. Using BERT$_{\textsc {large}}$ and RoBERTa allows us to investigate whether the performance of the target LM correlates with the performance of the source LM. We evaluate our models on two cross-lingual zero-shot tasks: (1) Cross-lingual Natural Language Inference (XNLI) and (2) dependency parsing. We evaluate our approach for six target languages: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).

Which six target languages were evaluated?

French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).

Knowledge graphs BIBREF0 enable structured access to world knowledge and form a key component of several applications like search engines, question answering systems and conversational assistants. Knowledge graphs are typically interpreted as comprising of discrete triples of the form (entityA, relationX, entityB) thus representing a relation (relationX) between entityA and entityB. However, one limitation of only a discrete representation of triples is that it does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph. Consequently, one popular alternative is to learn dense continuous representations of entities and relations by embedding them in latent continuous vector spaces, while seeking to model the inherent structure of the knowledge graph. Most knowledge graph embedding methods can be classified into two major classes: one class which operates purely on triples like RESCAL BIBREF1 , TransE BIBREF2 , DistMult BIBREF3 , TransD BIBREF4 , ComplEx BIBREF5 , ConvE BIBREF6 and the second class which seeks to incorporate additional information (like multi-hops) BIBREF7 . Learning high-quality knowledge graph embeddings can be quite challenging given that (a) they need to effectively model the contextual usages of entities and relations (b) they would need to be useful for a variety of predictive tasks on knowledge graphs. In this paper, we present a new type of knowledge graph embeddings called Dolores that are both deep and contextualized. Dolores learns both context-independent and context-dependent embeddings of entities and relations through a deep neural sequential model. Figure 1 illustrates the deep contextualized representations learned. Note that the contextually independent entity embeddings (see Figure 1 ) reveal three clusters of entities: writers, philosophers, and musicians. The contextual dependent embeddings in turn effectively account for specific relations. In particular, the context-dependent representations under the relation nationality now nicely cluster the above entities by nationality namely Austrians, Germans, and British/Irish. Similarly Figure 1 shows contextual embeddings given the relation place-lived. Note that these embeddings correctly capture that even though Beethoven and Brahms being Germans, they lived in Vienna and are closer to other Austrian musicians like Schubert. Unlike most knowledge graph embeddings like TransD, TransE BIBREF2 , BIBREF4 etc. which are typically learned using shallow models, the representations learned by Dolores are deep: dependent on an entire path (rather than just a triple), are functions of internal states of a Bi-Directional LSTM and composed of representations learned at various layers potentially capturing varying degrees of abstractions. Dolores is inspired by recent advances in learning word representations (word embeddings) from deep neural language models using Bi-Directional LSTMs BIBREF8 . In particular, we derive connections between the work of Peters et al. ( BIBREF8 ) who learn deep contextualized word embeddings from sentences using a Bi-Directional LSTM based language model and random walks on knowledge graphs. These connections enable us to propose new “deep contextualized” knowledge graph embeddings which we call Dolores embeddings. Knowledge Embeddings learned using Dolores can easily be used as input representations for predictive models on knowledge graphs. More importantly, when existing predictive models use input representations for entities and relations, we can easily replace those representations with Dolores representations and significantly improve the performance of existing models. Specifically, we show that Dolores embeddings advance the state-of-the-art models on various tasks like link prediction, triple classification and missing relation type prediction. To summarize, our contributions are as follows: However, one limitation of only a discrete representation of triples is that it does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph.

What is the limit to representing triples only discretely?

It does not easily enable one to infer similarities and potential relations among entities which may be missing in the knowledge graph.

Tomar attracts many tourists because of its varied monuments. These include: Castle and Convent of the Order of Christ – Unesco World Heritage Site: An ensemble of 12th to 16th century architecture and art, it is the main monument of the city and one of the most important in Portugal. Aqueduct of Pegões – Built between 1503 and 1614 to bring water to the convent of Christ in Tomar under command of king Philip I, the aqueduct is 6 kilometers long and in places reaches a height of 30 meters. It is the biggest and most important construction of Philip I in Portugal. Church of Santa Maria do Olival: This 13th century Gothic church was built as a burial ground for the Knights Templar and their treasure. Synagogue of Tomar: the best preserved mediaeval synagogue of Portugal (and one of two pre-expulsion Synagogues in the country), built in the mid-15th century the Jewish community of Tomar. Since 1939 it houses the Jewish Museum Abraão Zacuto, with pieces related to Jewish history in Portugal. Church of Saint John the Baptist (São João Baptista): The main church of Tomar is located in the main square of the town, in front of the Municipality (17th century) and a modern statue of Gualdim Pais. The church was built between the 15th and 16th centuries. In addition to its architectural interest it is noted for several panels painted in the 1530s by one of Portugal's most renowned Renaissance artists, Gregório Lopes. Chapel of Our Lady of the Conception (Nossa Senhora da Conceição): Chapel built between 1532 and 1540 in pure Renaissance style, begun by João de Castilho and finished by Diogo de Torralva. It was intended to be the burial chapel of King John III. Church and Convent of Saint Iria: An early 16th century building located near the Nabão river. Museu dos Fosforos (Matchbox Museum) – The biggest private matchbox collection in Europe. Museu de Arte Moderna – Colecção José Augusto França (art gallery) Casa Museu Fernando Lopes Graça Casa dos Cubos (art gallery) The streets and squares of the picturesque centre of Tomar are organised following a chessboard pattern, a rare feature for a mediaeval city, instituted by Prince Henry the Navigator, which later inspired the pattern used for the rebuilding of Lisbon after the earthquake in 1755. Scattered throughout the town there are many interesting houses with Renaissance, Baroque and Romantic façades. By the river Nabão, near the bridge, there is a park and garden that offer views of the city and surroundings.

Give me a comma separated list of the main attractions in Tomar.

Castle and Convent of the Order of Christ, Aqueduct of Pegões, Church of Santa Maria do Olival, Synagogue of Tomar, Church of Saint John the Baptist, Chapel of Our Lady of the Conception, Church and Convent of Saint Iria, Museu dos Fosforos (Matchbox Museum), Museu de Arte Moderna, Casa Museu Fernando Lopes Graça, Casa dos Cubos (art gallery)

What is the benefit of cinnamon

Helps control blood sugar levels and prevent heart disease

The winningest quarterback in NFL history, Brady won 251 regular season games and 35 postseason games for a combined 286 wins. Brady holds a .754 winning percentage, which is the highest among NFL quarterbacks who have started 100 games. He is the only NFL quarterback to win 200 regular season games and his 35 postseason victories are more than twice those of any other quarterback. Brady is also the only NFL quarterback to win all 16 regular season games, which he accomplished in 2007. In 2021, Brady became one of four quarterbacks to defeat all 32 NFL teams. Brady has the most Pro Bowl selections in NFL history at 15 and was named league MVP three times in 2007, 2010, and 2017. His 2010 MVP was awarded in an unanimous decision; the first time this distinction was achieved. Brady was also named Male Athlete of the Year in 2007, making him third NFL player to receive the award, joining Joe Montana. Brady, Montana, and Patrick Mahomes are the only players to win multiple NFL MVP and Super Bowl MVP awards.

Please give me a brief summary of this paragraph about Tom Brady's NFL accomplishments.

Brady is the winningest quarterback in NFL history. He holds numerous records in the regular season and postseason. He also holds distinctions as the only quarterback to accomplish certain feats, such as winning all 16 regular season games in 2007.

What is a safe temperature for a hot bath?

Its advisable to stay between 101 and 105 degrees fahrenheit for a hot bath to ensure you don't burn your skin.

In 1961 which Henry Mancini record won Grammy record of year

Moon River

My cat is constantly meowing by the door after I let her out once, but it's dangerous for cats to be outdoors unsupervised. What are some ways I can train her to stop meowing?

There are a few different ways you can train your cat to stop meowing: - Get a clicker, and whenever she pauses her meowing, click the clicker and give her a treat. The clicker trains her to associate her behavior with the reward. - Find something else to entertain her, such as a cat toy, or a video of wildlife. - Build an outdoor enclosure for her, accessible from a cat door. This will allow her to go outside, but in a safe and controller environment.

Pre-trained language models such as BERT BIBREF0 have significantly improved the accuracy of various language processing tasks. However, we cannot apply BERT to language generation tasks as is because its model structure is not suitable for language generation. Several pre-trained seq-to-seq models for language generation BIBREF1, BIBREF2 based on an encoder-decoder Transformer model, which is a standard model for language generation, have recently been proposed. These models have achieved blackstate-of-the-art results in various language generation tasks, including abstractive summarization. However, when generating a summary, it is essential to correctly predict which part of the source text should be included in the summary. Some previous studies without pre-training have examined combining extractive summarization with abstractive summarization BIBREF3, BIBREF4. Although pre-trained seq-to-seq models have achieved higher accuracy compared to previous models, it is not clear whether modeling “Which part of the source text is important?” can be learned through pre-training. blackThe purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task. Our main contributions are as follows: We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder. We proposed a new combination model, the conditional summarization model with important tokens (CIT), in which a token sequence extracted by a saliency model is explicitly given to a seq-to-seq model as an additional input text. We evaluated the combination models on the CNN/DM BIBREF5 and XSum BIBREF6 datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets. We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder.

How many combinations of pre-trained seq-to-seq and token-level saliency models did the authors investigate?

Nine.

Imagine you are the last person on Mars. Write a diary entry describing your thoughts and feelings.

Today was a beautiful day on Mars. The sun was shining brightly and the air was crisp and cool. I was able to take in the beauty of the planet and the sky, which I had never seen before. I felt like I was on another world, but I knew I was still in the same place. I took a walk around the dome and marveled at the beauty of the planet. It was so peaceful and I felt like I was in a dream. I smiled as I thought about how much I was learning about Mars and its inhabitants. It was a great day. Pity it could not last.

Mount St. Helens (known as Lawetlat'la to the indigenous Cowlitz people, and Loowit or Louwala-Clough to the Klickitat) is an active stratovolcano located in Skamania County, Washington, in the Pacific Northwest region of the United States. It lies 52 miles (83 km) northeast of Portland, Oregon, and 98 miles (158 km) south of Seattle. Mount St. Helens takes its English name from that of the British diplomat Lord St Helens, a friend of explorer George Vancouver who surveyed the area in the late 18th century. The volcano is part of the Cascade Volcanic Arc, a segment of the Pacific Ring of Fire. The Mount St. Helens major eruption of May 18, 1980 remains the deadliest and most economically destructive volcanic event in U.S. history. Fifty-seven people were killed; 200 homes, 47 bridges, 15 miles (24 km) of railways, and 185 miles (298 km) of highway were destroyed. A massive debris avalanche, triggered by a magnitude 5.1 earthquake, caused a lateral eruption that reduced the elevation of the mountain's summit from 9,677 ft (2,950 m) to 8,363 ft (2,549 m), leaving a 1 mile (1.6 km) wide horseshoe-shaped crater. The debris avalanche was 0.6 cubic miles (2.5 km3) in volume. The 1980 eruption disrupted terrestrial ecosystems near the volcano. By contrast, aquatic ecosystems in the area greatly benefited from the amounts of ash, allowing life to multiply rapidly. Six years after the eruption, most lakes in the area had returned to their normal state. After its 1980 eruption, the volcano experienced continuous volcanic activity until 2008. Geologists predict that future eruptions will be more destructive, as the configuration of the lava domes requires more pressure to erupt. However, Mount St. Helens is a popular hiking spot and it is climbed year-round. In 1982, the Mount St. Helens National Volcanic Monument was established by President Ronald Reagan and the U.S. Congress.

Is Mount Saint Helens still active?

Mount St. Helens, located in the sate of Washington is considered an active volcano.

Bristol is the largest city in South West England, and as such is a centre for culture, research and higher education in the region. The city is home to a prestigious "red brick university" (University of Bristol) and a high-ranking "new university" (University of the West of England). The city is also noted for its investment in the sciences and engineering, particularly its ties to the aerospace industry.

Is Bristol a large city

Indeed. Bristol is the largest city in South West England, and as such is a centre for culture, research and higher education in the region. The city is home to a prestigious "red brick university" (University of Bristol) and a high-ranking "new university" (University of the West of England). The city is also noted for its investment in the sciences and engineering, particularly its ties to the aerospace industry.

The Avro Canada CF-105 Arrow was a delta-winged interceptor aircraft designed and built by Avro Canada. The CF-105 held the promise of Mach 2 speeds at altitudes exceeding 50,000 feet (15,000 m) and was intended to serve as the Royal Canadian Air Force's (RCAF) primary interceptor into the 1960s and beyond. The Arrow was the culmination of a series of design studies begun in 1953 that examined improved versions of the Avro Canada CF-100 Canuck. After considerable study, the RCAF selected a dramatically more powerful design, and serious development began in March 1955. The aircraft was intended to be built directly from the production line, skipping the traditional hand-built prototype phase. The first Arrow Mk. 1, RL-201, was rolled out to the public on 4 October 1957, the same day as the launch of Sputnik I. Flight testing began with RL-201 on 25 March 1958, and the design quickly demonstrated excellent handling and overall performance, reaching Mach 1.9 in level flight. Powered by the Pratt & Whitney J75, another four Mk. 1s were completed, RL-202, RL-203, RL-204 and RL-205. The lighter and more powerful Orenda Iroquois engine was soon ready for testing, and the first Mk 2 with the Iroquois, RL-206, was ready for taxi testing in preparation for flight and acceptance tests by RCAF pilots by early 1959.

Given the lineage of this historical military aircraft, when was the Avro Arrow first flown and what were the operating characteristics of that flight?

The Avro Canada RL-201 Arrow was first flown in a test flight on March 25, 1958. During that flight, handling and performance proved excellent and the aircraft reached speeds of up to Mach 1.9 in level flight.

Identify which car manufacturer is Japanese or American: Isuzu, Cadillac

Isuzu is Japanese, Cadillac is American

While the XGBoost model often achieves higher accuracy than a single decision tree, it sacrifices the intrinsic interpretability of decision trees. For example, following the path that a decision tree takes to make its decision is trivial and self-explained, but following the paths of hundreds or thousands of trees is much harder. To achieve both performance and interpretability, some model compression techniques allow transforming an XGBoost into a single "born-again" decision tree that approximates the same decision function

Given this paragraph, what are the potential advantages or disadvantages of using XGBoost versus a single decision tree?

One advantage of using XGBoost over a single decision tree is that it frequently achieves higher accuracy. One disadvantage is that it sacrifices interpretability because it is much more difficult to follow the path of hundreds or thousands of trees.

Kamerion Wimbley (born October 13, 1983) is a former American football outside linebacker and defensive end in the National Football League (NFL). He was drafted by the Cleveland Browns in the first round of the 2006 NFL Draft with the 13th overall pick. He also played for the Oakland Raiders and Tennessee Titans before retiring in 2015. He played college football at Florida State University. Early years Attending Wichita Northwest High School in Kansas, Wimbley played defensive end, linebacker, quarterback, receiver and punter. He earned Parade and SuperPrep All-America honors during his senior season and was considered to be one of the top high school athletes in the nation. He played in the 2002 U.S. Army All-American Bowl. Considered a four-star recruit by Rivals.com, Wimbley was ranked 58th overall among football prospects of the class of 2002. He selected Florida State over Notre Dame, Nebraska, Oklahoma and Tennessee. Wimbley graduated from Northwest High School in December 2001 (a semester early) and enrolled at Florida State in the spring of 2002.

Which positions did Kamerion Wimbley play in High school?

Kamerion Wimbley played Football in multiple positions in Wichita Northwest High School in Kansas, including: defensive end, linebacker, quarterback, receiver and punter.

A Hot Brown sandwich (sometimes known as a Louisville Hot Brown or Kentucky Hot Brown) is an American hot sandwich originally created at the Brown Hotel in Louisville, Kentucky, by Fred K. Schmidt in 1926. It is a variation of traditional Welsh rarebit and was one of two signature sandwiches created by chefs at the Brown Hotel shortly after its founding in 1923. It was created to serve as an alternative to ham and egg late-night dinners. Ingredients The Hot Brown is an open-faced sandwich of turkey breast and ham and bacon, covered in creamy Mornay sauce and baked or broiled until the bread is crisp and the sauce begins to brown. Cheddar cheese or American cheese may be added for the sauce. Alternatives for garnishes include tomatoes, mushroom slices, and, very rarely, canned peaches. Some Hot Browns also include ham with the turkey, and either pimentos or tomatoes over the sauce, and imitation Hot Browns sometimes substitute a commercial cheese sauce instead of the Mornay. When Fred K. Schmidt created the Hot Brown, its sliced roast turkey was a rarity, as turkey was usually reserved for holiday feasts. The original Hot Brown included the sliced turkey on an open-faced white toast sandwich, with Mornay sauce covering it, with a sprinkling of Parmesan cheese, completed by being oven-broiled until bubbly. Pimento and bacon strips were then added to it. After its debut, it quickly became the choice of 95% of the Brown Hotel's restaurant customers. The dish is a local specialty and favorite of the Louisville area, and is popular throughout Kentucky. It was long unavailable at its point of origin, as the Brown Hotel was shuttered from 1971 to 1985.

What's a Louisville hot brown?

The Hot Brown is an open faced sandwich that is a local speciality throughout Kentucky. It was originally served in 1926 at Louisville's Brown Hotel. The sandwich is a spin on the traditional Welsh Rarebit. It's turkey breast, served open face on white bread, topped with Mornay sauce and run under the broiler until hot and bubbly. Some variations include ham with the turkey, as well as pimentos or tomatoes.

These vessels were the first true ocean-going ships, and are an important part of the Austronesian heritage. They were the vessels that enabled the Austronesian expansion from Taiwan into the islands of both the Indian and Pacific Ocean from around 3000 BC. They comprise the bulk of traditional boats in Island Southeast Asia, Island Melanesia, Micronesia, Polynesia, and Madagascar. They have spread to other cultures Austronesians came into contact with, notably in Sri Lanka and southern India as well as in the coast of East Africa. In modern times, outrigger vessels are used in the sport of sailing. Catamaran and trimaran configurations are also widely used for high speed craft.

From the text, extract the locations in which outrigger vessels are present in. Separate them with commas.

Taiwan, Island Southeast Asia, Island Melanesia, Micronesia, Polynesia, Madagascar, Sri Lanka, southern India, East Africa

How can you tell how old a tree is?

The size of tree has a direct correlation on how old the tree is. The wider the diameter and circumference of the tree the older it is. When a tree is cut down there will be inner rings in the stump that describe the tree's age as well. A tree's diameter will increase in size about 2.5 centimeters per year. You can estimate the age of a tree by measuring the circumference of the tree in centimeters and then dividing it by 2.5 centimeters. It will be important to measure as close to the ground as possible to get the most accurate measurement for the tree's age.

On a hiking trip where we expect to walk many miles, categorize each of the the following as either 'heavy', 'not heavy' or 'somewhat heavy'. A tent, a compass, a sleeping bag, a camping stove, a cooler full of food.

A tent is heavy. A compass is not heavy. A sleeping bag is not heavy. A camping stove is somewhat heavy. A cooler full of food is heavy.

Why do people like soccer?

People like soccer as it is a creative worldwide game. Unlike other sports that have frequent stoppages and timeouts soccer has two 45 minute halves. On top of that it is a very creative sport as it requires 11 team members to work in union to achieve a common goal. Everybody can play soccer no matter your age or athletic level so it is extremely appealing to many. Additionally, unlike other sports that require significant associated cost, soccer is rather cheap, all you need is a ball.

In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer. For FBFans dataset, we learned the 50-dimensional word embeddings on the whole dataset using GloVe BIBREF21 to capture the word semantics; for CreateDebate dataset we used the publicly available English 50-dimensional word embeddings, pre-trained also using GloVe. These word embeddings were fixed in the training process. The learning rate was set to 0.03. All user and topic embeddings were randomly initialized in the range of [-0.1 0.1]. Matrix embeddings for users and topics were sized at 250 ( INLINEFORM0 ); vector embeddings for users and topics were set to length 10. We applied the LDA topic model BIBREF22 on the FBFans dataset to determine the latent topics with which to build topic embeddings, as there is only one general known topic: nuclear power plants. We learned 100 latent topics and assigned the top three topics for each post. For the CreateDebate dataset, which itself constitutes four topics, the topic labels for posts were used directly without additionally applying LDA. For the FBFans data we report class-based f-scores as well as the macro-average f-score ( INLINEFORM0 ) shown in equation EQREF19 . DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are the average precision and recall of the three class. We adopted the macro-average f-score as the evaluation metric for the overall performance because (1) the experimental dataset is severely imbalanced, which is common for contentious issues; and (2) for stance classification, content in minor-class posts is usually more important for further applications. For the CreateDebate dataset, accuracy was adopted as the evaluation metric to compare the results with related work BIBREF7 , BIBREF9 , BIBREF12 . In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer.

What is used as the optimizer in the UTCNN training process?

AdaGrad.

1909.07593

We propose a design for EI to efficiently learn rich implicit structures for exponentially many combinations of targets to predict. To do so, we explain the process to assign scores to each edge $e$ from our neural architecture. The three yellow boxes in Figure FIGREF14 compute scores for rich implicit structures from the neural architecture consisting of LSTM and self-attention. Given an input token sequence $\mathbf {x}=\lbrace x_1,x_2,\cdots ,x_{n}\rbrace $ of length $n$, we first compute the concatenated embedding $\mathbf {e}_k=[\mathbf {w}_k;\mathbf {c}_k]$ based on word embedding $\mathbf {w}_k$ and character embedding $\mathbf {c}_k$ at position $k$. As illustrated on the left part in Figure FIGREF14, we then use a Bi-directional LSTM to encode context features and obtain hidden states $\mathbf {h}_k=\mathrm {BiLSTM}(\mathbf {e_1},\mathbf {e_2}, \cdots , \mathbf {e_n})$. We use two different linear layers $f_t$ and $f_s$ to compute scores for target and sentiment respectively. The linear layer $f_t$ returns a vector of length 4, with each value in the vector indicating the score of the corresponding tag under the BMES tagging scheme. The linear layer $f_s$ returns a vector of length 3, with each value representing the score of a certain polarity of $+,0,-$. We assign such scores to each type of edge as follows: As illustrated in Figure FIGREF14, we calculate $\mathbf {a}_k$, the output of self-attention at position $k$: where $\alpha _{k,j}$ is the normalized weight score for $\mathbf {\beta }_{k,j}$, and $\mathbf {\beta }_{k,j}$ is the weight score calculated by target representation at position $k$ and contextual representation at position $j$. In addition, $W$ and $b$ as well as the attention matrix $U$ are the weights to be learned. Such a vector $\mathbf {a}_k$ encodes the implicit structures between the word $x_k$ and each word in the remaining sentence. FLOAT SELECTED: Figure 4: Neural Architecture The three yellow boxes in Figure FIGREF14 compute scores for rich implicit structures from the neural architecture consisting of LSTM and self-attention. Given an input token sequence $\mathbf {x}=\lbrace x_1,x_2,\cdots ,x_{n}\rbrace $ of length $n$, we first compute the concatenated embedding $\mathbf {e}_k=[\mathbf {w}_k;\mathbf {c}_k]$ based on word embedding $\mathbf {w}_k$ and character embedding $\mathbf {c}_k$ at position $k$. As illustrated on the left part in Figure FIGREF14, we then use a Bi-directional LSTM to encode context features and obtain hidden states $\mathbf {h}_k=\mathrm {BiLSTM}(\mathbf {e_1},\mathbf {e_2}, \cdots , \mathbf {e_n})$. We use two different linear layers $f_t$ and $f_s$ to compute scores for target and sentiment respectively. The linear layer $f_t$ returns a vector of length 4, with each value in the vector indicating the score of the corresponding tag under the BMES tagging scheme. The linear layer $f_s$ returns a vector of length 3, with each value representing the score of a certain polarity of $+,0,-$. We assign such scores to each type of edge as follows: As illustrated in Figure FIGREF14, we calculate $\mathbf {a}_k$, the output of self-attention at position $k$: where $\alpha _{k,j}$ is the normalized weight score for $\mathbf {\beta }_{k,j}$, and $\mathbf {\beta }_{k,j}$ is the weight score calculated by target representation at position $k$ and contextual representation at position $j$. In addition, $W$ and $b$ as well as the attention matrix $U$ are the weights to be learned. Such a vector $\mathbf {a}_k$ encodes the implicit structures between the word $x_k$ and each word in the remaining sentence. FLOAT SELECTED: Figure 4: Neural Architecture

Which model is used to capture the implicit structure?

The answers are shown as follows: * Bi-directional LSTM * self-attention

Jeans are a type of pants or trousers made from denim or dungaree cloth. Often the term "jeans" refers to a particular style of trousers, called "blue jeans", with copper-riveted pockets which were invented by Jacob W. Davis in 1871 and patented by Jacob W. Davis and Levi Strauss on May 20, 1873. Prior to the patent, the term "blue jeans" had been long in use for various garments (including trousers, overalls, and coats), constructed from blue-colored denim. "Jean" also references a (historic) type of sturdy cloth commonly made with a cotton warp and wool weft (also known as "Virginia cloth"). Jean cloth can be entirely cotton as well, similar to denim. Originally designed for miners, modern jeans were popularized as casual wear by Marlon Brando and James Dean in their 1950s films, particularly The Wild One and Rebel Without a Cause, leading to the fabric becoming a symbol of rebellion among teenagers, especially members of the greaser subculture. From the 1960s onwards, jeans became common among various youth subcultures and subsequently young members of the general population. Nowadays, they are one of the most popular types of specialty trousers in Western culture. Historic brands include Levi's, Lee, and Wrangler.

Who invented the Jeans

Jeans are a type of pants or trousers made from denim or dungaree cloth. Often the term "jeans" refers to a particular style of trousers, called "blue jeans", with copper-riveted pockets which were invented by Jacob W. Davis in 1871 and patented by Jacob W. Davis and Levi Strauss on May 20, 1873. Prior to the patent, the term "blue jeans" had been long in use for various garments (including trousers, overalls, and coats), constructed from blue-colored denim. "Jean" also references a (historic) type of sturdy cloth commonly made with a cotton warp and wool weft (also known as "Virginia cloth"). Jean cloth can be entirely cotton as well, similar to denim. Originally designed for miners, modern jeans were popularized as casual wear by Marlon Brando and James Dean in their 1950s films, particularly The Wild One and Rebel Without a Cause, leading to the fabric becoming a symbol of rebellion among teenagers, especially members of the greaser subculture. From the 1960s onwards, jeans became common among various youth subcultures and subsequently young members of the general population. Nowadays, they are one of the most popular types of specialty trousers in Western culture. Historic brands include Levi's, Lee, and Wrangler.

This is a list of all occasions where a football player was sent off from a FIFA World Cup match due to a foul or misconduct, either as a direct expulsion (red card) or as a second caution (yellow card) within the match. This list includes all dismissals since the first World Cup in 1930. The use of physical red and yellow cards to respectively indicate dismissals and cautions is a later invention, having been introduced at the 1970 tournament. Only players are listed, even if they were at the substitutes' bench at the time of the sending off. Managers and other technical staff members are not covered. Statistics Plácido Galindo was the first player to be sent off in a World Cup match, playing for Peru against Romania in a 1930 match officiated by Alberto Warnken. Although physical red cards were introduced from 1970, they were not put in practice until the 1974 World Cup, when referee Doğan Babacan sent off Chile's Carlos Caszely during a match against West Germany. Two players have received red cards twice: Cameroon's Rigobert Song (1994 and 1998) and France's Zinedine Zidane (1998 and 2006). Five dismissals have taken place during final matches: Argentina's Pedro Monzón and Gustavo Dezotti (both 1990), France's Marcel Desailly (1998) and Zinedine Zidane (2006), and John Heitinga of the Netherlands (2010). Uruguay's José Batista received the quickest red card, in the first minute in a game against Scotland in 1986. A few expulsions either took place after the final whistle of the match or were awarded to players at the substitutes' bench (or both), and thus did not reduce the number of players on the pitch during the game. Two of them, that of Leandro Cufré of Argentina (vs Germany, 2006) and that of Denzel Dumfries of the Netherlands (vs Argentina, 2022), took place after the penalty shoot-outs of knockout matches, making them the latest red cards. Three goalkeepers have been sent off in the tournament: Italy's Gianluca Pagliuca (1994), South Africa's Itumeleng Khune (2010), and Wales's Wayne Hennessey (2022). The match with the most dismissals was Portugal vs Netherlands of 2006, when referee Valentin Ivanov dished out 4 red cards, 2 for each team. The referee who has sent off the most players is Arturo Brizio Carter, 7 red cards, in 5 different matches (also a record). The team that has the greatest number of expulsed players is Brazil, with 11 – in 9 different matches, a record shared with Argentina. The team whose opponents have received the most red cards is Germany (incl. West Germany), with 20 – in 18 different matches, also a record. The 2006 World Cup had the highest amount of red cards: a total of 28 players were sent off (in 20 matches, also a record).

Who was the first player sent off during a soccer match?

Plácido Galindo was the first player to be sent off in a World Cup match, playing for Peru against Romania in a 1930 match officiated by Alberto Warnken. Although physical red cards were introduced from 1970, they were not put in practice until the 1974 World Cup, when referee Doğan Babacan sent off Chile's Carlos Caszely during a match against West Germany.

Classify the following numbers as 'odd' or 'even' - 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16.

'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even'.

How many strings does a bass guitar have?

Most bass guitars have four strings. Because the bass is designed for lower frequency than standard (usually six-string) guitars, the bass guitar strings are thicker and heavier than standard guitar strings. Some basses are made with five or even six strings. This allows the bassist to hit higher or lower notes than the standard bass guitar range, which can allow more creative freedom. Four strings is still considered the standard for bass guitars, though.

Massimo Bray is an academic publisher and a magazine editor. He was on the editorial board of the Italian Institute of Human Sciences until 1994. He is the cofounder of Notte della Taranta, which is among the most popular music festivals in Europe. He launched a blog on the Italian-language version of the Huffington Post.

Based on this paragraph about Massimo Bray, what language do you think he writes in?

Massimo Bray likely writes in Italian.

Why is March 14th Pi day?

The number Pi is 3.14159265... and so on. The first three numbers, 3.14, correspond with the 14th of March in the American calendar notation, with March being the third month.

What are the top 5 rock bands of all time?

The top 5 rock bands of all time is a difficult question as most individuals have different tastes and like various versions of rock. There could be an argument that U2 belongs on that list given it's extensive catalog of music and presence on a global stage. Others that might be considered include Led Zeppelin, The Grateful Dead, KISS, Radiohead. This questions really comes down to what bands have grossed the most from Album sales & Touring receipts. Any interesting question no doubt but comes down to their commercial success as well as the likes & dislikes of the fan.

Name some of the popular gaming consoles in the 2020s

PlayStation 5, Xbox series X, Nintendo Switch, and Steam Deck

Reasoning is part of our every day routine: we hear Natural Language (NL) sentences, we participate in dialogues, we read books or legal documents. Successfully understanding, participating or communicating with others in these situations presupposes some form of reasoning: about individual sentences, whole paragraphs of legal documents, small or bigger pieces of dialogue and so on. The human reasoning performed in these different situations cannot be explained by a single rigid system of reasoning, plainly because reasoning is performed in different ways in each one of them. Consider the following example: Three representatives are needed. If a human reasoner with expert knowledge was to interpret the above utterance in a legal context, s/he will most probably judge that a situation where more than three references are provided could be compatible with the semantics of the utterance. To the contrary, if the same reasoner was to interpret the above as part of a casual, everyday conversation, then three would most likely be interpreted as exactly three, making the same situation incompatible with the utterance. In this paper, we want to focus on precise inference, inference which is either performed by experts or normal people after taking some time to consider the inferences that follow or not from a set of premises. The problem one encounters in testing systems of this type of reasoning is the fact that no large scale datasets of this sort of inference exist. The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets. The commonly used datasets for systems fit for this type of precise reasoning, i.e. logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.

What are the commonly used datasets fit for this type of precise reasoning mentioned in this paper?

Logical systems based on some model of formal semantics for Natural Language (NL), are the FraCaS test suite and the Recognizing Textual Entailment (RTE) datasets.

Neural machine translation (NMT) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 is a deep learning-based method for translation that has recently shown promising results as an alternative to statistical approaches. NMT systems directly model the probability of the next word in the target sentence simply by conditioning a recurrent neural network on the source sentence and previously generated target words. While both simple and surprisingly accurate, NMT systems typically need to have very high capacity in order to perform well: Sutskever2014 used a 4-layer LSTM with 1000 hidden units per layer (herein INLINEFORM0 ) and Zhou2016 obtained state-of-the-art results on English INLINEFORM1 French with a 16-layer LSTM with 512 units per layer. The sheer size of the models requires cutting-edge hardware for training and makes using the models on standard setups very challenging. This issue of excessively large networks has been observed in several other domains, with much focus on fully-connected and convolutional networks for multi-class classification. Researchers have particularly noted that large networks seem to be necessary for training, but learn redundant representations in the process BIBREF6 . Therefore compressing deep models into smaller networks has been an active area of research. As deep learning systems obtain better results on NLP tasks, compression also becomes an important practical issue with applications such as running deep learning models for speech and translation locally on cell phones. Existing compression methods generally fall into two categories: (1) pruning and (2) knowledge distillation. Pruning methods BIBREF7 , BIBREF8 , BIBREF9 , zero-out weights or entire neurons based on an importance criterion: LeCun1990 use (a diagonal approximation to) the Hessian to identify weights whose removal minimally impacts the objective function, while Han2016 remove weights based on thresholding their absolute values. Knowledge distillation approaches BIBREF0 , BIBREF10 , BIBREF1 learn a smaller student network to mimic the original teacher network by minimizing the loss (typically INLINEFORM0 or cross-entropy) between the student and teacher output. In this work, we investigate knowledge distillation in the context of neural machine translation. We note that NMT differs from previous work which has mainly explored non-recurrent models in the multi-class prediction setting. For NMT, while the model is trained on multi-class prediction at the word-level, it is tasked with predicting complete sequence outputs conditioned on previous decisions. With this difference in mind, we experiment with standard knowledge distillation for NMT and also propose two new versions of the approach that attempt to approximately match the sequence-level (as opposed to word-level) distribution of the teacher network. This sequence-level approximation leads to a simple training procedure wherein the student network is trained on a newly generated dataset that is the result of running beam search with the teacher network. We run experiments to compress a large state-of-the-art INLINEFORM0 LSTM model, and find that with sequence-level knowledge distillation we are able to learn a INLINEFORM1 LSTM that roughly matches the performance of the full system. We see similar results compressing a INLINEFORM2 model down to INLINEFORM3 on a smaller data set. Furthermore, we observe that our proposed approach has other benefits, such as not requiring any beam search at test-time. As a result we are able to perform greedy decoding on the INLINEFORM4 model 10 times faster than beam search on the INLINEFORM5 model with comparable performance. Our student models can even be run efficiently on a standard smartphone. Finally, we apply weight pruning on top of the student network to obtain a model that has INLINEFORM6 fewer parameters than the original teacher model. We have released all the code for the models described in this paper. As a result we are able to perform greedy decoding on the 2 × 500 model 10 times faster than beam search on the 4 × 1000 model with comparable performance.

How many times is performing greedy decoding on the 2 × 500 model than on the 4 × 1000 model?

10 times.

What is a Treasury Bond?

Treasury bonds (T-bonds) are government debt securities issued by the U.S. Federal government that have maturities greater than 20 years. T-bonds earn periodic interest until maturity, at which point the owner is also paid a par amount equal to the principal.

How are volcanoes formed?

Volcanoes are formed when magma from within the Earth's upper mantle works its way to the surface. At the surface, it erupts to form lava flows and ash deposits. Over time as the volcano continues to erupt, it will get bigger and bigger.

2003.07433

LAXARY includes a modified LIWC model to calculate the possible scores of each survey question using PTSD Linguistic Dictionary to fill out the PTSD assessment surveys which provides a practical way not only to determine fine-grained discrimination of physiological and psychological health markers of PTSD without incurring the expensive and laborious in-situ laboratory testing or surveys, but also obtain trusts of clinicians who are expected to see traditional survey results of the PTSD assessment. LAXARY includes a modified LIWC model to calculate the possible scores of each survey question using PTSD Linguistic Dictionary to fill out the PTSD assessment surveys which provides a practical way not only to determine fine-grained discrimination of physiological and psychological health markers of PTSD without incurring the expensive and laborious in-situ laboratory testing or surveys, but also obtain trusts of clinicians who are expected to see traditional survey results of the PTSD assessment.

How is LIWC incorporated into this system?

The answers are shown as follows: * to calculate the possible scores of each survey question using PTSD Linguistic Dictionary

How fast can an ostrich run?

An ostrich can run up to 56 mph (90 km/h). It is the fastest land animal after the cheetah.

Identify which animal is domesticated or wild: Sheep, Caracal

Sheep is domesticated, Caracal is wild

In the series A Song of Ice and Fire, who is the founder of House Lannister?

Lann the Clever

We proposed to leverage on linked entities to improve the performance of sequence-to-sequence models on neural abstractive summarization task. Linked entities are used to guide the decoding process based on the summary topic and commonsense learned from a knowledge base. We introduced Entity2Topic (E2T), a module that is easily attachable to any model using an encoder-decoder framework. E2T applies linked entities into the summarizer by encoding the entities with selective disambiguation and pooling them into one summary topic vector with firm attention mechanism. We showed that by applying E2T to a basic sequence-to-sequence model, we achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models. We showed that by applying E2T to a basic sequence-to-sequence model, we achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models.

What do the authors achieve by applying E2T to a basic sequence-to-sequence model?

They achieve significant improvements over the base model and consequently achieve a comparable performance with more complex summarization models.

1912.13109

We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. This dataset was important to employ Transfer Learning to our task since the number of labeled dataset was very small. Basic summary and examples of the data from the dataset are below: We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.

How is the dataset collected?

The answers are shown as follows: * A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al * HEOT obtained from one of the past studies done by Mathur et al

In particle physics, an elementary particle or fundamental particle is a subatomic particle that is not composed of other particles. Particles currently thought to be elementary include electrons, the fundamental fermions (quarks, leptons, antiquarks, and antileptons, which generally are matter particles and antimatter particles), as well as the fundamental bosons (gauge bosons and the Higgs boson), which generally are force particles that mediate interactions among fermions.A particle containing two or more elementary particles is a composite particle.

From the passage provided, extract the names of elementary particles and arrange them in alphabetical order

1. Antileptons 2. Antiquarks 3. Electrons 4. Gauge bosons 5. Higgs boson 6. Leptons 7. Quarks

What are several unique ways in which you could travel from Seattle, Washington to Miami Florida?

Driving a car, riding on a plane, riding on a train, running, walking, riding a donkey, riding a horse, crab walking, riding a boat.

What are the best woods for smoking?

Oak is the quintessential go-to for smoking meat. It is a great place to start if you are a newbie to smoking. It will lend a medium to a strong flavor that is seldom overpowering. - Best Meat to Smoke: Lamb, beef, brisket, and sausages Hickory is the most versatile choice as it can be used to smoke wood in many ways. Be careful, though, because too much hickory flavor will cause your meat to have a more bitter flavor. It has a sweet, savory, and hearty, a bit bacony. - Best Meat to Smoke: Larger cuts of ribs and pork shoulders, as well as almost all red meat and poultry. Maple is one of the most subtle smoking wood, it will impart a more subtle smoke flavor. Use maple for a sweet, light, mild smokiness. - Best Meat to Smoke: Poultry, pork, game foul Mesquite is a hardy wood packing intense flavor. It is recommended for grilling, or to be used in smaller quantities. It is famous for its strong, intense, and unique. - Best Meat to Smoke: Red Meat and for adding additional flavor when grilling Pecan will lend a rich, sweet, nutty flavor. In fact, the wood is so sweet that you may want to use a combination of it with another harder wood to balance out the flavor. - Best Meat to Smoke: Briskets, roasts, and ribs Applewood is mild and sweet, lending a mellow flavor. Apple smoke flavors take a while to permeate the meat, so anticipate several hours of smoking. - Best Meat to Smoke: Chicken, wild foul, pork Alder has a very light and sweet flavor profile. It lends a delicate and sweet characteristic. - Best Meat to Smoke: Fish, like salmon and other Pacific Northwestern types. Cherry is mild and fruity, and when mixed with other hardwood like hickory, the two flavors compliment each other for an amazing result. - Best Meat to Smoke: Chicken, turkey, ham