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Should technology and software coding subjects be mandatory in the curriculum of high school education?

Acknowledging the wide spread influence, impact, and disruption that enabling technologies are having on society and business, strong consideration should be made to modernize educational curriculums to include technology subjects. Objectives would be to improve awareness, exposure, and creative thinking to ultimately inspire further experimentation and innovation. Related to requiring students to learn language, science and math topics, strong consideration should me made to require students to gain base awareness, knowledge and coding skills. These foundational skills will become building blocks necessary to achieve life long successes and influence the pace of innovation across all industries and career paths.

Categorize these super heroes as 'Marvel' or 'DC Comics'

Thor, Flash, Iron Man, Super Man, Wonder Woman, Captain America

Do i must to hug my wife

Hugging Improves Relationships. It's a deeper, more intimate level of connection that you don't get from talking. The second reason is that physical touch causes the release of the love hormone oxytocin, which improves bonding and trust and makes people feel closer to one another. You should hug your spouse every day as an act of showing your love to them. Giving a hug is one of the simplest ways to show your love. Anytime, anywhere a hug can is a healthy reminder to your spouse of why you love them so much.

We conducted our experiments using the freely available open source software package UMLS::Similarity BIBREF16 version 1.47. This package takes as input two terms (or UMLS concepts) and returns their similarity or relatedness using the measures discussed in Section SECREF2 . Correlation between the similarity measures and human judgments were estimated using Spearman's Rank Correlation ( INLINEFORM0 ). Spearman's measures the statistical dependence between two variables to assess how well the relationship between the rankings of the variables can be described using a monotonic function. We used Fisher's r-to-z transformation BIBREF17 to calculate the significance between the correlation results. Correlation between the similarity measures and human judgments were estimated using Spearman’s Rank Correlation (ρ).

What was used for estimating the correlation between the similarity measures and human judgments?

Spearman’s Rank Correlation (ρ).

1705.03261

We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. The predictions confusion matrix is shown in table TABREF46 . The DDIs other than false being classified as false makes most of the classification error. It may perform better if a classifier which can tells true and false DDI apart is trained. We leave this two-stage classifier to our future work. Another phenomenon is that the “Int” type is often classified as “Effect”. The “Int” sentence describes there exists interaction between two drugs and this information implies the two drugs' combination will have good or bed effect. That's the reason why “Int” and “Effect” are often obfuscated. We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction.

By how much does their model outperform existing methods?

Answer with content missing: (Table II) Proposed model has F1 score of 0.7220 compared to 0.7148 best state-state-of-the-art result.

The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk (so price changes are random) and thus cannot be predicted. The concept can be traced to French broker Jules Regnault who published a book in 1863, and then to French mathematician Louis Bachelier whose Ph.D. dissertation titled "The Theory of Speculation" (1900) included some remarkable insights and commentary. The same ideas were later developed by MIT Sloan School of Management professor Paul Cootner in his 1964 book The Random Character of Stock Market Prices. The term was popularized by the 1973 book A Random Walk Down Wall Street by Burton Malkiel, a professor of economics at Princeton University, and was used earlier in Eugene Fama's 1965 article "Random Walks In Stock Market Prices", which was a less technical version of his Ph.D. thesis. The theory that stock prices move randomly was earlier proposed by Maurice Kendall in his 1953 paper, The Analysis of Economic Time Series, Part 1: Prices.

Where did the random walk hypothesis come from?

It is said that the random walk hypothesis originated back in the mid 1800s from a French financier named Jules Regnault. The random walk hypothesis notes that stock price movements are fully random and cannot be easily forecast.

We downloaded questions and answers via the Smartvote API. The downloaded data cover 175 communal, cantonal and national elections between 2011 and 2020. All candidates in an election who participate in Smartvote are asked the same set of questions, but depending on the locale they see translated versions of the questions. They can answer each question with either `yes', `rather yes', `rather no', or `no'. They can supplement each answer with a comment of at most 500 characters. The questions asked on Smartvote have been edited by a team of political scientists. They are intended to cover a broad range of political issues relevant at the time of the election. A detailed documentation of the design of Smartvote and the editing process of the questions is provided by BIBREF12. We merged the two labels on each pole into a single label: `yes' and `rather yes' were combined into `favor'; `rather no', or `no' into `against`. This improves the consistency of the data and the comparability to previous stance detection datasets. We did not further preprocess the text of the comments. As the API does not provide the language of comments, we employed a language identifier to automatically annotate this information. We used the langdetect library BIBREF13. For each responder we classified all the comments jointly, assuming that responders did not switch code during the answering of the questionnaire. We applied the identifier in a two-step approach. In the first run we allowed the identifier to output all 55 languages that it supports out of the box, plus Romansh, the fourth official language in Switzerland. We found that no Romansh comments were detected and that all unexpected outputs were misclassifications of German, French or Italian comments. We further concluded that little or no Swiss German comments are in the dataset: If they were, some of them would have manifested themselves in the form of misclassifications (e.g. as Dutch). In the second run, drawing from these conclusions, we restricted the identifier's output to English, French, German and Italian. We pre-filtered the questions and answers to improve the quality of the dataset. To keep the domain of the data surveyable, we set a focus on national-level questions. Therefore, all questions and corresponding answers pertaining to national elections were included. In the context of communal and cantonal elections, candidates have answered both local questions and a subset of the national questions. Of those elections, we only considered answers to the questions that also had been asked in a national election. Furthermore, they were only used to augment the training set while the validation and test sets were restricted to answers from national elections. We discarded the less than 20 comments classified as English. Furthermore, instances that met any of the following conditions were filtered from the dataset: Question is not a closed question or does not address a clearly defined political issue. No comment was submitted by the candidate or the comment is shorter than 50 characters. Comment starts with “but” or a similar indicator that the comment is not a self-contained statement. Comment contains a URL. In total, a fifth of the original comments were filtered out. The questions have been organized by the Smartvote editors into categories (such as “Economy”). We further consolidated the pre-defined categories into 12 broad topics (Table TABREF7). The dataset is shared under a CC BY-NC 4.0 license. Copyright remains with www.smartvote.ch. Given the sensitive nature of the data, we increase the anonymity of the data by hashing the respondents' IDs. No personal attributes of the respondents, such as their party affiliation, are included in the dataset. We provide a data statement BIBREF15 in Appendix SECREF8. All candidates in an election who participate in Smartvote are asked the same set of questions, but depending on the locale they see translated versions of the questions.

Are all candidates in an election who participate in Smartvote asked the same set of questions?

Yes.

What is the difference between an asset and a liability.

The difference between an asset and a liability is that an asset generally provides value over the long term while a liability is an expense or debt owed.

1908.10383

As shown in Table TABREF13, there is almost no discrimination among the last four methods under ROUGE-1 F1, and their rankings under ROUGE-1/2/L are quite different. In contrast, FAR shows that UnifiedSum(E) covers the most facets. Although FAR is supposed to be favored as FAMs are already manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference, we rank UnifiedSum(E), NeuSum, and Lead-3 in Table TABREF15. The order of the 1st rank in the human evaluation coincides with FAR. FAR also has higher Spearman's coefficient $\rho $ than ROUGE (0.457 vs. 0.44, n=30, threshold=0.362 at 95% significance). Although FAR is supposed to be favored as FAMs are already manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference, we rank UnifiedSum(E), NeuSum, and Lead-3 in Table TABREF15. The order of the 1st rank in the human evaluation coincides with FAR. FAR also has higher Spearman's coefficient $\rho $ than ROUGE (0.457 vs. 0.44, n=30, threshold=0.362 at 95% significance).

How do they evaluate their proposed metric?

The answers are shown as follows: * manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference,

What are symptoms of ‘Attention Deficit Hyperactivity Disorder (ADHD)’?

ADHD symptoms can be divided into three major categories: behavioral, cognitive, and mood. ADHD behavioral symptoms include aggression, excitability, fidgeting, hyperactivity, impulsivity, irritability, and repeated words or actions. Symptoms of cognitive impairment include absentmindedness, difficulty focusing, forgetfulness, attention problems, and short attention spans. There are many mood-related symptoms, such as anger, anxiety, boredom, excitement, and mood swings.

What links Willie Brant, Lech Walesa, Yasser Arafat

Nobel Peace Prize

Bungeoppang This fish-shaped bun filled with sweet red beans is a classic street snack. It is known to have come from Japan in the 1930s. Taiyaki which was famous in Japan became Bungeoppang. In Japanese, "Tai" means sea bream, and "yaki" means roasted. So this is a cheap street snack which imitates the more expensive Japanese food. It was the most common street food in the 1950s and 1960s, after the Japanese colonial period. It has appeared again since the 1990s. Boong o bbang.jpg Eomuk Fish cake is a mixture of fish meat and wheat flour. The hot sauce flavored with soy sauce can be addictive to many. Eomuk is also a typical Japanese food. It used to be called oden; Japanese oden is boiled tofu, fish cake, konjac, jelly, and boiled egg on a skewer. It was after the time of enlightenment in 1876 that the eomuk tang (fish cake soup) was brought to Korea. It entered Korea at the port of Bu-san and became a widespread Korean street food. As the home of fish cake history, Busan boasts that its fish cake is the best in Korea. Eomuk-kkochi.jpg Hotteok Hotteok is a traditional street food in South Korea. It is commonly eaten in the winter. Normally, hotteok is made of dough filled with cinnamon-flavored raw sugar. Nowadays, there are varieties of hotteok with nuts like peanuts. or a colored hotteok with green tea powder and corn flour. Hotteok.jpg Hoppang Hoppang means steamed bun in Korean. A steamed bun is made from flour, usually from the United States, and red beans. Ingredients such as vegetables, meat, sweet pumpkin, curry and pizza are added, and additional variants on the hoppang theme are constantly being developed. It can be found both on the street and at convenience stores. Hoppang (inside).jpg Tteokbokki Tteokbokki is stir-fried rice cake, a traditional Korean food. There is a history of food similar to tteokbokki in a book called '食療纂要' compiled at the time of Chosun, Sejo in 1460. Before kochujang tteokbokki, there was food similar to tteokbokki in the old court. In the 1800s cookbook "是議全書", there is a record that "I ate white rice cake with beef sirloin, soy sauce, onion, rice cake and so on." It re-emerged in the late 1960s, with a Kochujang (red chili paste) sauce. Tteok-bokki.jpg Gyeran-ppang Gyeran-ppang (egg bread) is a warm street snack sold throughout South Korea. The fluffy, oblong-shaped loaf of bread is sweet and savory with a whole egg inside the pancake dough. Gyeranppang by travel oriented.jpg Dak-kkochi Dak-kkochi, called Korean chicken skewers, is a popular South Korean street food consisting of small pieces of chicken and scallions grilled on a skewer. Street Food - Chicken skewers - Dakkochi (닭꼬치) (10585858164).jpg Galbi Galbi is a term which means rib and typically come from pork or chicken. It is a popular dish where the meat is marinated with a mixture of soy sauce, garlic, sugar, and sesame seeds. It is usually placed on a stick since the stick is readily available to dispose and to cook on a grill. Of course, beef galbi can be used to make soup (galbitang) and steamed galbi (galbijjim). But these dishes, while excellent in their own right, are overshadowed by their grilled leader (Kim 2018). Galbi with bamboo leaves.jpg Sundae Sundae is a type of blood sausage that is mixed with pig intestine and stuffed with cellophane noodles, vegetables, and meat. It's usually served cut up into bite size pieces with lung and liver from the pig on the side as a garnish. Sundae.jpg Gunbam Gunbam is a street food that is considered one of the most popular snacks in winter. It is a roasted chestnut baked in a brazier, which tastes both savory and sweet.

Please give me a list of popular street foods available in South Korea

Bungeoppang, Eomuk, Hotteok, Hoppang, Tteokbokki, Gyeranppang, Dak-kkochi, Galbi, Sundae, Gunbam

What was the Long Night?

According to legends in Westeros, the Long Night was a darkness that fell over the known world and the Westeroi were subjected to not only a generation-long winter, but also the terror and destruction brought on by the Others. A similar telling comes from across the sea where the people of Essos experienced a winter that froze their waters and the Yi Ti described a generation where none saw the sun.

Identify which instrument is string or woodwind: Stritch, Zhu

Zhu is string, Stritch is woodwind.

What is CrossFit?

CrossFit is a workout program that was originally created by Greg Glassman in California. It focuses on high intensity constantly varied functional movements. People who do CrossFit go to CrossFit affiliated gyms to complete their workouts. The workouts are called WOD which stands for Workout Of the Day. These workouts vary in length, cardio intensity, and complexity of movements. CrossFit also combines elements of Olympic weightlifting, gymnastics, and traditional strength training. While CrossFit is an excellent way to get and stay in shape, it has received backlash from the media for the increased potential for injury that is also associated with the programming. Even with the media backlash, CrossFit has remained a popular syle of workout for those looking to get in shape.

Identify which animal species is alive or extinct: Amargasaurus, Black Rhino

Black Rhino is alive, Amargasaurus is extinct.

1908.11860

To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. On the restaurants dataset, this corresponds to an absolute improvement of $2.2\%$ compared to the previous state-of-the-art method BERT-PT. Language model finetuning produces a larger improvement on the restaurants dataset. We think that one reason for that might be that the restaurants domain is underrepresented in the pre-training corpora of BERTBASE. Generally, we find that language model finetuning helps even if the finetuning domain does not match the evaluation domain. We think the reason for this might be that the BERT-base model is pre-trained more on knowledge-based corpora like Wikipedia than on text containing opinions. Another finding is that BERT-ADA Joint performs better on the laptops dataset than BERT-ADA Rest, although the unique amount of laptop reviews are the same in laptops- and joint-corpora. We think that confusion can be created when mixing the domains, but this needs to be investigated further. We also find that the XLNet-base baseline performs generally stronger than BERT-base and even outperforms BERT-ADA Lapt with an accuracy of $79.89\%$ on the laptops dataset. In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training. We think the reason for this might be that syntactical relationships between the aspect-target and the phrase expressing sentiment polarity as well as knowing the sentiment-polarity itself are sufficient to solve the ATSC task in many cases. To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training.

What are the performance results?

The answers are shown as follows: * results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset * new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively.

Why might I want to stay at home on a Friday night?

- You're feeling under the weather - The weather outside is stormy and dangerous - All your friends are busy and can't hang out with you - There's nothing fun going on - It's too crowded out there - You need to binge watch the latest show

During the Middle Ages, the Gran Tavola (Italian for "Great Table") was the largest Sienese bank; it was one of the most powerful banks in Europe from 1255 to 1298. The Gran Tavola has been called "the greatest bank of the thirteenth century" as well as "one of the largest commercial and banking enterprises in Europe". The main branches of the Gran Tavola during the mid-thirteenth century were in Pisa, Bologna, Genoa, Marseille, and Paris.

List all the years mentioned in the following passage

1255, 1298

What was the first country to guarantee freedom of worship

Transylvania

Recently, neural machine translation (NMT) has gained popularity in the field of machine translation. The conventional encoder-decoder NMT proposed by Cho2014 uses two recurrent neural networks (RNN): one is an encoder, which encodes a source sequence into a fixed-length vector, and the other is a decoder, which decodes the vector into a target sequence. A newly proposed attention-based NMT by DzmitryBahdana2014 can predict output words using the weights of each hidden state of the encoder by the attention mechanism, improving the adequacy of translation. Even with the success of attention-based models, a number of open questions remain in NMT. Tu2016 argued two of the common problems are over-translation: some words are repeatedly translated unnecessary and under-translation: some words are mistakenly untranslated. This is due to the fact that NMT can not completely convert the information from the source sentence to the target sentence. Mi2016a and Feng2016 pointed out that NMT lacks the notion of coverage vector in phrase-based statistical machine translation (PBSMT), so unless otherwise specified, there is no way to prevent missing translations. Another problem in NMT is an objective function. NMT is optimized by cross-entropy; therefore, it does not directly maximize the translation accuracy. Shen2016 pointed out that optimization by cross-entropy is not appropriate and proposed a method of optimization based on a translation accuracy score, such as expected BLEU, which led to improvement of translation accuracy. However, BLEU is an evaluation metric based on n-gram precision; therefore, repetition of some words may be present in the translation even though the BLEU score is improved. To address to problem of repeating and missing words in the translation, tu2016neural introduce an encoder-decoder-reconstructor framework that optimizes NMT by back-translation from the output sentences into the original source sentences. In their method, after training the forward translation in a manner similar to the conventional attention-based NMT, they train a back-translation model from the hidden state of the decoder into the source sequence by a new decoder to enforce agreement between source and target sentences. In order to confirm the language independence of the framework, we experiment on two parallel corpora of English-Japanese and Japanese-English translation tasks using encode-decoder-reconstructor. Our experiments show that their method offers significant improvement in BLEU scores and alleviates the problem of repeating and missing words in the translation on English-Japanese translation task, though the difference is not significant on Japanese-English translation task. In addition, we jointly train a model of forward translation and back-translation without pre-training, and then evaluate this model. As a result, the encoder-decoder-reconstructor can not be trained well without pre-training, so it proves that we have to train the forward translation model in a manner similar to the conventional attention-based NMT as pre-training. The main contributions of this paper are as follows: Even with the success of attention-based models, a number of open questions remain in NMT. Tu et al. (2016) argued two of the common problems are over-translation: some words are repeatedly translated unnecessary and under-translation: some words are mistakenly untranslated.

What are the unanswered questions about NMT?

Over-translation and under-translation.

The Grateful Dead was founded in the San Francisco Bay Area amid the rise of the counterculture of the 1960s. The founding members were Jerry Garcia (lead guitar, vocals), Bob Weir (rhythm guitar, vocals), Ron "Pigpen" McKernan (keyboards, harmonica, vocals), Phil Lesh (bass, vocals), and Bill Kreutzmann (drums). Members of the Grateful Dead, originally known as the Warlocks, had played together in various Bay Area ensembles, including the traditional jug band Mother McCree's Uptown Jug Champions. Lesh was the last member to join the Warlocks before they changed their name to the Grateful Dead; replacing Dana Morgan Jr., who had played bass for a few gigs. Drummer Mickey Hart and non-performing lyricist Robert Hunter joined in 1967. With the exception of McKernan, who died in 1973, and Hart, who took time off from 1971 to 1974, the core of the band stayed together for its entire 30-year history. The other official members of the band are Tom Constanten (keyboards; 1968–1970), John Perry Barlow (non-performing lyricist; 1971–1995), Keith Godchaux (keyboards, occasional vocals; 1971–1979), Donna Godchaux (vocals; 1972–1979), Brent Mydland (keyboards, vocals; 1979–1990), and Vince Welnick (keyboards, vocals; 1990–1995). Bruce Hornsby (accordion, piano, vocals) was a touring member from 1990 to 1992, as well as a guest with the band on occasion before and after the tours.

Given this reference text about the Grateful Dead, tell me who their keyboard players were over the years.

Ron "Pigpen" McKernan was the original keyboard player of the Grateful Dead. Others to play keyboards for the Grateful Dead include Tom Constanten, Keith Godchaux, Brent Mydland and Vince Welnick.

Who won the best new artist as well as award for the Best Pop Vocal Album in 64th Annual Grammy Awards

Olivia Rodrigo

How did actor James Dean die?

Car accident

Classify these vehicles by class. Mercedes A-Class, BMW M4, Toyota Camry, Ford F150, CyberTruck

Mercedes A-Class: Car BMW M4: Car Toyota Camry: Car Ford F150: Truck CyberTruck: Truck

1710.10609

We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2 iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid). We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2 iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid).

How do they generate the synthetic dataset?

using generative process

What does "digital nomads" mean?

Digital nomads are professionals who use technology and the internet to do their jobs while traveling and living a nomadic life. Digital nomads work remotely, instead of going to a company's office or headquarters.

What would it be like to be a pirate?

If you were a pirate, your mission would be to find treasure. The question is between the vastness and depth of the sea, how could you ever find such a treasure? There are no treasure maps. You could look in history books to find coordinates of where they believe ships have crashed in the past, take those coordinates, and search for those sites. However, you may find the site only to realize the boat never carried any treasure that could stand the test of time under the water. The treasure may have wasted away.

We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ). For LibriSpeech, we trained Jasper DR 10x5 using our NovoGrad optimizer for 400 epochs. We achieve SOTA performance on the test-clean subset and SOTA among end-to-end speech recognition models on test-other. We trained a smaller Jasper 10x3 model with SGD with momentum optimizer for 400 epochs on a combined WSJ dataset (80 hours): LDC93S6A (WSJ0) and LDC94S13A (WSJ1). The results are provided in Table TABREF29 . We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ).

Where did the team collect the read speech datasets in their experiments?

From LibriSpeech and Wall Street Journal (WSJ).

1710.06536

Most of the previous works in aspect term extraction have either used conditional random fields (CRFs) BIBREF9 , BIBREF10 or linguistic patterns BIBREF7 , BIBREF11 . Both of these approaches have their own limitations: CRF is a linear model, so it needs a large number of features to work well; linguistic patterns need to be crafted by hand, and they crucially depend on the grammatical accuracy of the sentences. In this chapter, we apply an ensemble of deep learning and linguistics to tackle both the problem of aspect extraction and subjectivity detection. In this chapter, we apply an ensemble of deep learning and linguistics to tackle both the problem of aspect extraction and subjectivity detection.

How are aspects identified in aspect extraction?

The answers are shown as follows: * apply an ensemble of deep learning and linguistics t

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. In the Internet era, thanks to the mechanism of sharing in social networks, propaganda campaigns have the potential of reaching very large audiences BIBREF0, BIBREF1, BIBREF2. Propagandist news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others (cf. Section SECREF3). Whereas proving intent is not easy, we can analyse the language of a claim/article and look for the use of specific propaganda techniques. Going at this fine-grained level can yield more reliable systems and it also makes it possible to explain to the user why an article was judged as propagandist by an automatic system. With this in mind, we organised the shared task on fine-grained propaganda detection at the NLP4IF@EMNLP-IJCNLP 2019 workshop. The task is based on a corpus of news articles annotated with an inventory of 18 propagandist techniques at the fragment level. We hope that the corpus would raise interest outside of the community of researchers studying propaganda. For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis. We hope that the corpus would raise interest outside of the community of researchers studying propaganda. For example, the techniques related to fallacies and the ones relying on emotions might provide a novel setting for researchers interested in Argumentation and Sentiment Analysis.

Why use this corpus as a basis?

They hope that the corpus would raise interest outside of the community of researchers studying propaganda.

The Last of Us is an action-adventure game played from a third-person perspective. The player traverses post-apocalyptic environments such as towns, buildings, forests, and sewers to advance the story. The player can use firearms, improvised weapons, hand-to-hand combat, and stealth to defend against hostile humans and cannibalistic creatures infected by a mutated strain of the Cordyceps fungus. For most of the game, the player takes control of Joel, a man tasked with escorting a young girl, Ellie, across the United States. The player also controls Ellie throughout the game's winter segment and briefly controls Joel's daughter, Sarah, in the opening sequence.

Extract and list the names of characters a player can control in The Last of Us mentioned in the paragraph. Also describe relationships between those characters.

In the Last of Us, a player can control Joel, Ellie (who is being escorted by Joel across the United States), and Sarah (who is Joel's daughter).

Pixar has produced 26 feature films, starting with Toy Story (1995), which is the first fully computer-animated feature film; its most recent film was Lightyear (2022). The studio has also produced many short films. As of July 2019, its feature films have earned approximately $14 billion at the worldwide box office, with an average worldwide gross of $680 million per film. Toy Story 3 (2010), Finding Dory (2016), Incredibles 2 (2018), and Toy Story 4 (2019) are all among the 50 highest-grossing films of all time. Incredibles 2 is the studio's highest grossing film as well as the fourth-highest-grossing animated film of all time, with a gross of $1.2 billion; the other three also grossed over $1 billion. Moreover, 15 of Pixar's films are in the 50 highest-grossing animated films of all time. Pixar has earned 23 Academy Awards, 10 Golden Globe Awards, and 11 Grammy Awards, along with numerous other awards and acknowledgments. Its films are frequently nominated for the Academy Award for Best Animated Feature, since its inauguration in 2001, with eleven winners being Finding Nemo (2003), The Incredibles (2004), Ratatouille (2007), WALL-E (2008), Up (2009), Toy Story 3 (2010), Brave (2012), Inside Out (2015), Coco (2017), Toy Story 4 (2019), and Soul (2020). The six nominated films that did not win are Monsters, Inc. (2001), Cars (2006), Incredibles 2 (2018), Onward (2020), Luca (2021) and Turning Red (2022). While Cars 2 (2011), Monsters University (2013), The Good Dinosaur (2015), Finding Dory (2016), Cars 3 (2017) and the aforementioned Lightyear were not nominated. In addition, Toy Story 3 and Up were nominated for the Academy Award for Best Picture. On February 10, 2009, Pixar executives John Lasseter, Brad Bird, Pete Docter, Andrew Stanton, and Lee Unkrich were presented with the Golden Lion award for Lifetime Achievement by the Venice Film Festival. The physical award was ceremoniously handed to Lucasfilm's founder, George Lucas.

List the Pixar movies that were nominated for Academy Awards but did not win.

Monsters, Inc. (2001), Cars (2006), Incredibles 2 (2018), Onward (2020), Luca (2021), Turning Red (2022)

The Daisy Bates House is a historic house at 1207 West 28th Street in Little Rock, Arkansas, USA. It is significant as the home of Arkansas NAACP president Daisy Bates, and for its use as a command post for those working to desegregate the Little Rock Central High School during the desegregation crisis of 1957–1958. It was a sanctuary for the nine students involved. It was declared a National Historic Landmark in 2001. Description The Daisy Bates House is set in a mid-20th-century residential area south of downtown Little Rock, Arkansas. It is a typical 1950s ranch house, one story in height, measuring about 39 feet (12 m) by 51 feet (16 m). It has a wood-frame structure, with its exterior finished in brick veneer. It is topped by a hip roof with broad eaves, which also shelters a carport on the north and west side of the house. The foundation and the carport deck are poured concrete. The main elevation, facing north, has two picture windows with sidelights. The main entrance to the living room is located in the carport, as is a secondary entrance to the kitchen. The main floor has a typical ranch plan, with a public area consisting of living room, dining room, and kitchen, with a hallway leading to two bedrooms. There are two bathrooms. The basement is partially finished, housing a recreation room and half-bath in addition to the house's mechanical systems. The attic is finished and is accessible via pulldown stairs.

What is the Daisy Bates House

The Daisy Bates House is a historic house at 1207 West 28th Street in Little Rock, Arkansas, USA. It is significant as the home of Arkansas NAACP president Daisy Bates, and for its use as a command post for those working to desegregate the Little Rock Central High School during the desegregation crisis of 1957–1958. It was a sanctuary for the nine students involved. It was declared a National Historic Landmark in 2001. The Daisy Bates House is set in a mid-20th-century residential area south of downtown Little Rock, Arkansas. It is a typical 1950s ranch house, one story in height, measuring about 39 feet (12 m) by 51 feet (16 m). It has a wood-frame structure, with its exterior finished in brick veneer. It is topped by a hip roof with broad eaves, which also shelters a carport on the north and west side of the house. The foundation and the carport deck are poured concrete.

I started a new company named Greystone Inc. What do we do?

- Investment banking for weird guys only - Aerospace consulting - Pasta and pasta accessory global logistics - Political lobbying for the mining industry

Speech signals are analyzed based on the automatic detection of onset and offset transitions, which model the difficulties of the patients to start/stop the movement of the vocal folds. The detection of the transitions is based on the presence of the fundamental frequency of speech in short-time frames, as it was shown in BIBREF7. The border between voiced and unvoiced frames is detected, and 80 ms of the signal are taken to the left and to the right, forming segments with 160 ms length. The transition segments are modeled with two different approaches: (1) a baseline model based on hand-crafted features, which are classified using an SVM, and (2) a model based on time-frequency representations used as input to train a CNN, which then will be used for the transfer learning strategy. Further details are given in the following subsections. The transition segments are modeled with two different approaches: (1) a baseline model based on hand-crafted features, which are classified using an SVM, and (2) a model based on time-frequency representations used as input to train a CNN, which then will be used for the transfer learning strategy. Further details are given in the following subsections.

What are the two approaches they take for transition segments?

(1) a baseline model based on hand-crafted features, which are classified using an SVM, and (2) a model based on time-frequency representations used as input to train a CNN, which then will be used for the transfer learning strategy.

Why does the divide and average method for computing square roots work?

The divide and average method for computing square roots starts with a guess of the square root. For example, to compute the square root of 8, we might start with 3, since we know 9 is the square of 3. Given a guess, the method is a formula for generating the next guess. The formula states the next guess should be average of the current guess and the square (the number for which you want compute the square root) divided by the guess. Returning to our example, the next guess is (3 + 8/3)/2 = 17/6. Applying this formula repeatedly yields a guess which is closer and closer to actual square root. The reason this works is that limit of the sequence of guesses generated by this formula is the square root. To see this, first recognize that in the limit, next guess must be equal to the current guess. The guesses get closer and closer until, after an infinite number of steps, they are the same. Using a variable X to represent the limit and applying the formula yields an equation which can be solved for X via algebra. The solution is that X must be square root.

Write a short strategy on how to increase your chances to win a chess game.

The game of chess includes thinking several moves ahead of the opponent. You have to start by getting to the strategic squares on the board faster than the opponent, which directly results in having an upper hand for the rest of the game. The next best step is to protect your pieces while taking out several of your opponent's pieces. The pawn structure needs to be aligned in a way that protects the king at all times and clears the way for the queen to strategically advance forward into the opponent's side. Another strategy is by making the move of casting that pushes the king to the corner as opposed to being exposed in the front once pawns are taken out.

Which is a bird or fish: Yellow wagtail, Yellowback fusilier

Yellow wagtail is a bird, Yellowback fusilier is a fish

Huallacancha or Huallacanecha (possibly from Quechua walla mountain range, kancha enclosure, enclosed place, yard, a frame, or wall that encloses) is a mountain in the Pariacaca mountain range in the Andes of Peru, about 5,500 metres (18,045 ft) high. It is situated in the Junín Region, Yauli Province, on the border of the districts Suitucancha and Yauli. Huallacancha lies south-east of the mountain Qarwachuku, west of the mountain Ukhu Qhata and north of the mountain Wayllakancha.

What is Huallacancha?

Huallacancha is a mountain in the Andes of Peru that is approximately 18,045 ft high.

Who won the 2018 NHL Stanley Cup Finals?

The Washington Capitals won the 2018 Stanley Cup on June 8th, 2018. They defeated the Las Vegas Golden Nights 4 games to 1 in a best of 7 game series. The Capitals were coached by Barry Trotz and led by team captain, Alex Ovechkin, who won the Conn Smythe Trophy as Most Valuable Player.

The Battle of Grunwald, Battle of Žalgiris, or First Battle of Tannenberg, was fought on 15 July 1410 during the Polish–Lithuanian–Teutonic War. The alliance of the Crown of the Kingdom of Poland and the Grand Duchy of Lithuania, led respectively by King Władysław II Jagiełło (Jogaila), and Grand Duke Vytautas, decisively defeated the German Teutonic Order, led by Grand Master Ulrich von Jungingen. Most of the Teutonic Order's leadership were killed or taken prisoner. Although defeated, the Teutonic Order withstood the subsequent siege of the Malbork Castle and suffered minimal territorial losses at the Peace of Thorn (1411), with other territorial disputes continuing until the Treaty of Melno in 1422. The order, however, never recovered their former power, and the financial burden of war reparations caused internal conflicts and an economic downturn in the lands controlled by them. The battle shifted the balance of power in Central and Eastern Europe and marked the rise of the Polish–Lithuanian union as the dominant regional political and military force. The battle was one of the largest in medieval Europe. The battle is viewed as one of the most important victories in the histories of Poland and Lithuania. It is also commemorated in Ukraine and Belarus. It has been used as a source of romantic legends and national pride, becoming a larger symbol of struggle against foreign invaders. During the 20th century, the battle was used in Nazi German and Soviet propaganda campaigns. Only in recent decades have historians moved towards a dispassionate, scholarly assessment of the battle, reconciling the previous narratives, which differed widely by nation.

What was the result and impact of the First Battle of Tannenberg?

The Teutonic Order was defeated in the First Battle of Tannenberg and the subsequent decline of the Teutonic Order led to Polish–Lithuanian union becoming the dominant regional force.

We now apply our dogmatism classifier to a larger dataset of posts, examining how dogmatic language shapes the Reddit community. Concretely, we apply the BOW+LING model trained on the full Reddit dataset to millions of new unannotated posts, labeling these posts with a probability of dogmatism according to the classifier (0=non-dogmatic, 1=dogmatic). We then use these dogmatism annotations to address four research questions. Concretely, we apply the BOW+LING model trained on the full Reddit dataset to millions of new unannotated posts, labeling these posts with a probability of dogmatism according to the classifier (0=non-dogmatic, 1=dog- matic).

How is the model tested？

The BOW+LING model is applied on the full Reddit dataset to millions of new unannotated posts.

Tell me which of these are types of desserts: flan, steak, tofu, broccoli, cookies, Cheetos, banana bread, ice cream, giblets, macaron, doughnut, a toothbrush

Flan, cookies, banana bread, ice cream, macaron, doughnut

Beneficence is a concept in research ethics that states that researchers should have the welfare of the research participant as a goal of any clinical trial or other research study. The antonym of this term, maleficence, describes a practice that opposes the welfare of any research participant. According to the Belmont Report, researchers are required to follow two moral requirements in line with the principle of beneficence: do not harm and maximize possible benefits for research while minimizing any potential harm on others. The concept that medical professionals and researchers would always practice beneficence seems natural to most patients and research participants, but in fact, every health intervention or research intervention has potential to harm the recipient. There are many different precedents in medicine and research for conducting a cost–benefit analysis and judging whether a certain action would be a sufficient practice of beneficence, and the extent to which treatments are acceptable or unacceptable is under debate. Despite differences in opinion, there are many concepts on which there is wide agreement. One is that there should be community consensus when determining best practices for dealing with ethical problems.

Given this reference text about beneficence, what can I do to ensure compliance to the beneficence concept?

Ensure you are not harming your research participants, while maximizing benefits and minimizing any potential harm. Engage with your community to determine best practices when encountering ethical concerns. This should ensure your compliance with beneficence.

1911.03584

The theorem is proven constructively by selecting the parameters of the multi-head self-attention layer so that the latter acts like a convolutional layer. In the proposed construction, the attention scores of each self-attention head should attend to a different relative shift within the set $\Delta \!\!\!\!\Delta _K = \lbrace -\lfloor K/2 \rfloor , \dots , \lfloor K/2 \rfloor \rbrace ^2$ of all pixel shifts in a $K\times K$ kernel. The exact condition can be found in the statement of Lemma UNKREF15. The theorem is proven constructively by selecting the parameters of the multi-head self-attention layer so that the latter acts like a convolutional layer.

How they prove that multi-head self-attention is at least as powerful as convolution layer?

The answers are shown as follows: * constructively by selecting the parameters of the multi-head self-attention layer so that the latter acts like a convolutional layer

Give me ideas on how to gain trust in workplace.

Value long-term relationships. Trust requires long-term thinking Be honest Honor your commitments Admit when you're wrong Communicate effectively Be vulnerable Be helpful Show people that you care

What US state has the greatest population?

California is the most populous American state.

Andrés de Jesús María y José Bello López (Spanish pronunciation: [anˈdɾes ˈβeʝo]; November 29, 1781 – October 15, 1865) was a Venezuelan-Chilean humanist, diplomat, poet, legislator, philosopher, educator and philologist, whose political and literary works constitute an important part of Spanish American culture. Bello is featured on the old 2,000 Venezuelan bolívar and the 20,000 Chilean peso notes.

Who is Andres Bello?

Andrés de Jesús María y José Bello López (Spanish pronunciation: [anˈdɾes ˈβeʝo]; November 29, 1781 – October 15, 1865) was a Venezuelan-Chilean humanist, diplomat, poet, legislator, philosopher, educator and philologist, whose political and literary works constitute an important part of Spanish American culture. Bello is featured on the old 2,000 Venezuelan bolívar and the 20,000 Chilean peso notes. In Caracas, where he was born, Andrés Bello was Simón Bolívar's teacher for a short period of time and participated in the process that led to Venezuelan independence. As a diplomat for the new independent government that he helped establish, he went with Luis López Méndez and Simón Bolívar on their first diplomatic mission to London. He lived in London from 1810 to 1829. In 1829, Bello went with his family to Chile. He was hired by the Chilean government and made great works in the field of law and humanities. In Santiago he held positions as a senator and a professor, as well as directing several local newspapers. As a legislator, he was the main promoter and editor of the Chilean Civil Code, one of the most innovative and influential American legal works of his time. In 1842, under his inspiration and with his decisive support, the University of Chile was created, an institution of which he became the first rector, staying in the post for more than two decades. Due to his great contributions, on 17 October 1832 he was given Chilean nationality through a law approved by the Congress.

How many times has Tiger Woods won the Masters tournament?

Tiger Woods has won the Masters 5 times.

Tell me whether each food is high in fat or low in fat: butter, mango, banana, rice, peanuts, cheese

Butter: High in Fat Mango: Low in Fat Banana: Low in Fat Rice: Low in Fat Peanuts: High in Fat Cheese: High in Fat

Classify each of the following as a NFL Team or NBA Team: Chicago Bears, Chicago Bulls, Denver Nuggets, Denver Broncos, Philadelphia 76ers, Philadelphia Eagles, Philadelphia Flyers, Orlando Magic, Tennessee Titans, AND1 Live Streetballers

The following are NFL Teams: Chicago Bears, Denver Broncos, Philadelphia Eagles, Tennessee Titans. The following are NBA Teams: Chicago Bulls, Denver Nuggets, Philadelphia 76ers, Orlando Magic The following are neither NFL Teams or NBA Teams: Philadelphia Flyers, AND1 Live Streetballers

Arrange these weapons from oldest to newest, based on the date they were first invented. Sword Nuclear Bomb Hand Axe Gatling Gun Rifle

The correct order is handaxe, sword, rifle, gatling gun, nuclear bomb. The handaxe is a Stone Age weapon that has existed for more than one million years and long predates homo sapiens. The earliest swords were invented around 3,300 BC in Europe during the beginning of the Bronze Age. The first rifles were likely developed during the 15th century, although their exact origins are unknown. The Gatling gun was invented in 1862 and was first used in battle during the US civil war. The nuclear bomb was invented in 1945 as part of the Manhattan Project.

John James Monckton (28 October 1938 – 29 June 2017) was an Australian backstroke swimmer who won a silver medal in the 100-metre event at the 1956 Summer Olympics in Melbourne. Although he set multiple world records, he never won an Olympic gold medal. An apprentice carpenter from the New England region of New South Wales, Monckton appeared to be primed to win gold at the 1956 Olympics. At the national team camp in Townsville before the games, he became the first person to swim 400-metre backstroke in under five minutes. Although it was not a regularly contested event at international level, it was a promising sign for the event to be included for medal competition in the Olympics. He also set world records in the 110- and 220-yard freestyle events. At the Olympics, Monckton was the fastest qualifier in the heats and semifinal, but was upstaged in the final by teammate David Theile. In the absence of Theile, who had retired after the Olympics to study medicine at university, Monckton dominated backstroke swimming, winning the 110-yard backstroke event at the 1958 British Empire and Commonwealth Games in Cardiff and also the 4×110-yard medley relay. Monckton then prepared for another Olympics in 1960 in Rome, with Theile deferring his studies to defend his Olympic title. Monckton again led the qualifying in the heats and semifinals. However, in the final, he misjudged the turn and broke a finger. He limped home in seventh position, with Theile successfully defending his title. Monckton continued competing in the hope of reaching a third Olympics in 1964, but retired after his performances began to deteriorate. He married Maureen Giles, an Australian swimmer at the 1956 Olympics. He was inducted into the Sport Australia Hall of Fame in 1999. The Monckton Aquatic Centre in his hometown of Armidale is named for him.

What events did John James Monckton set the world record for?

John James Monckton set world records for the 400-metre backstroke, 220-yard freestyle and 110-yard freestyle events.

Where does paprika come from?

Paprika is just red bell peppers that have been dried and ground into a fine powder.

1906.00378

We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction: CNN-mean: taking the similarity score of the averaged feature of the two image sets. CNN-avgmax: taking the average of the maximum similarity scores of two image sets. We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction: CNN-mean: taking the similarity score of the averaged feature of the two image sets. CNN-avgmax: taking the average of the maximum similarity scores of two image sets.

What baseline is used for the experimental setup?

The answers are shown as follows: * CNN-mean * CNN-avgmax

Feature ablation studies are conducted to assess the informativeness of a feature group by quantifying the change in predictive power when comparing the performance of a classifier trained with the all feature groups versus the performance without a particular feature group. We conducted a feature ablation study by holding out (sans) each feature group and training and testing the support vector model using a linear kernel and 5-fold, stratified cross-validation. We report the average F1-score from our baseline approach (all feature groups) and report the point difference (+ or -) in F1-score performance observed by ablating each feature set. By ablating each feature group from the full dataset, we observed the following count of features - sans lexical: 185, sans syntactic: 16,935, sans emotion: 16,954, sans demographics: 16,946, sans sentiment: 16,950, sans personality: 16,946, and sans LIWC: 16,832. In Figure 1, compared to the baseline performance, significant drops in F1-scores resulted from sans lexical for depressed mood (-35 points), disturbed sleep (-43 points), and depressive symptoms (-45 points). Less extensive drops also occurred for evidence of depression (-14 points) and fatigue or loss of energy (-3 points). In contrast, a 3 point gain in F1-score was observed for no evidence of depression. We also observed notable drops in F1-scores for disturbed sleep by ablating demographics (-7 points), emotion (-5 points), and sentiment (-5 points) features. These F1-score drops were accompanied by drops in both recall and precision. We found equal or higher F1-scores by removing non-lexical feature groups for no evidence of depression (0-1 points), evidence of depression (0-1 points), and depressive symptoms (2 points). Unsurprisingly, lexical features (unigrams) were the largest contributor to feature counts in the dataset. We observed that lexical features are also critical for identifying depressive symptoms, specifically for depressed mood and for disturbed sleep. For the classes higher in the hierarchy - no evidence of depression, evidence of depression, and depressive symptoms - the classifier produced consistent F1-scores, even slightly above the baseline for depressive symptoms and minor fluctuations of change in recall and precision when removing other feature groups suggesting that the contribution of non-lexical features to classification performance was limited. However, notable changes in F1-score were observed for the classes lower in the hierarchy including disturbed sleep and fatigue or loss of energy. For instance, changes in F1-scores driven by both recall and precision were observed for disturbed sleep by ablating demographics, emotion, and sentiment features, suggesting that age or gender (“mid-semester exams have me restless”), polarity and subjective terms (“lack of sleep is killing me”), and emoticons (“wide awake :(”) could be important for both identifying and correctly classifying a subset of these tweets. Unsurprisingly, lexical features (unigrams) were the largest contributor to feature counts in the dataset.

What is the largest contributor to the number of features in the dataset?

Lexical features.

The Alps (/ælps/) are the highest and most extensive mountain range in Europe, stretching approximately 1,200 km (750 mi) across seven Alpine countries (from west to east): France, Switzerland, Italy, Liechtenstein, Austria, Germany, and Slovenia. The Alpine arch extends from Nice on the western Mediterranean to Trieste on the Adriatic and Vienna at the beginning of the Pannonian Basin. The mountains were formed over tens of millions of years as the African and Eurasian tectonic plates collided. Extreme shortening caused by the event resulted in marine sedimentary rocks rising by thrusting and folding into high mountain peaks such as Mont Blanc and the Matterhorn. Mont Blanc spans the French–Italian border, and at 4,809 m (15,778 ft) is the highest mountain in the Alps. The Alpine region area contains 128 peaks higher than 4,000 m (13,000 ft).

Name the seven Alpine countries from West to East.

The seven Alpine countries (from West to East): France, Switzerland, Italy, Liechtenstein, Austria, Germany, and Slovenia.

Sentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by reviewers on a topic or an entity. Sentiment analysis can be leveraged in marketing, social media analysis, and customer service. Although many studies have been conducted for sentiment analysis in widely spoken languages, this topic is still immature for Turkish and many other languages. Neural networks outperform the conventional machine learning algorithms in most classification tasks, including sentiment analysis BIBREF0. In these networks, word embedding vectors are fed as input to overcome the data sparsity problem and make the representations of words more “meaningful” and robust. Those embeddings indicate how close the words are to each other in the vector space model (VSM). Most of the studies utilise embeddings, such as word2vec BIBREF1, which take into account the syntactic and semantic representations of the words only. Discarding the sentimental aspects of words may lead to words of different polarities being close to each other in the VSM, if they share similar semantic and syntactic features. For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task. Our main contribution is to create original and effective word vectors that capture syntactic, semantic, and sentimental characteristics of words, and use all of this knowledge in generating embeddings. We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review basis and create document vectors. We evaluate those embeddings on two datasets. The results show that we outperform the approaches which do not take into account the sentimental information. We also had better performances than other studies carried out on sentiment analysis in Turkish media. We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available. The paper is organised as follows. In Section 2, we present the existing works on sentiment classification. In Section 3, we describe the methods proposed in this work. The experimental results are shown and the main contributions of our proposed approach are discussed in Section 4. In Section 5, we conclude the paper. For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task.

Which two scores do they combine?

The unsupervised and supervised scores.

1707.06939

We recruited 176 AMT workers to participate in our conceptualization task. Of these workers, 90 were randomly assigned to the Control group and 86 to the AUI group. These workers completed 1001 tasks: 496 tasks in the control and 505 in the AUI. All responses were gathered within a single 24-hour period during April, 2017. These workers completed 1001 tasks: 496 tasks in the control and 505 in the AUI.

How many responses did they obtain?

The answers are shown as follows: * 1001

1602.07618

In order to understand what INLINEFORM0 is, we need to understand the mathematics of grammar. The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest... On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory! The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest... On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory!

Do they break down word meanings into elementary particles as in the standard model of quantum theory?

No.

Six Feet Under is an American drama television series created and produced by Alan Ball. It premiered on the premium network HBO in the United States on June 3, 2001, and ended on August 21, 2005, spanning 63 episodes across five seasons. It depicts the lives of the Fisher family, who run a funeral home in Los Angeles, along with their friends and lovers.

What is "Six Feet Under"?

Six Feet Under is an American drama television series created and produced by Alan Ball. It premiered on the premium network HBO in the United States on June 3, 2001, and ended on August 21, 2005, spanning 63 episodes across five seasons. It depicts the lives of the Fisher family, who run a funeral home in Los Angeles, along with their friends and lovers. The ensemble drama stars Peter Krause, Michael C. Hall, Frances Conroy, Lauren Ambrose, Freddy Rodriguez, Mathew St. Patrick, and Rachel Griffiths as the central characters. It was produced by Actual Size Films and The Greenblatt/Janollari Studio, and was shot on location in Los Angeles and in Hollywood studios. Six Feet Under received widespread critical acclaim, particularly for its writing and acting, and consistently drew high ratings for the HBO network. It is widely regarded as one of the greatest television series of all time. The show's finale has also been described as one of the greatest television series finales. The series won numerous awards, including nine Emmy Awards, three Screen Actors Guild Awards, three Golden Globe Awards, and a Peabody Award.

Gone West (March 10, 1984 – September 7, 2009) was an American Thoroughbred racehorse. Bred by William O. Reed, he was a son of the influential sire Mr. Prospector. His dam, Secrettame, was a daughter of 1973 U.S. Triple Crown winner Secretariat. Purchased by Alice du Pont Mills and raced under her Hickory Tree Stable banner, Gone West was conditioned for racing by U.S. Racing Hall of Fame trainer Woody Stephens.

Who bred the famous Gone West racehorse?

Gone West was bread by William O. Reed, the son of the influential sire Mr. Prospector.

The Universal Data Element Framework (UDEF) was a controlled vocabulary developed by The Open Group. It provided a framework for categorizing, naming, and indexing data. It assigned to every item of data a structured alphanumeric tag plus a controlled vocabulary name that describes the meaning of the data. This allowed relating data elements to similar elements defined by other organizations. UDEF defined a Dewey-decimal like code for each concept. For example, an "employee number" is often used in human resource management. It has a UDEF tag a.5_12.35.8 and a controlled vocabulary description "Employee.PERSON_Employer.Assigned.IDENTIFIER". UDEF has been superseded by the Open Data Element Framework (O-DEF).

Given a reference text about The Universal Data Element Framework (UDEF), tell me how the framework is used.

The UDEF is a framework for categorizing, naming, and indexing data.

1804.02233

Tweets related to Forex, specifically to EUR and USD, were acquired through the Twitter search API with the following query: “EURUSD”, “USDEUR”, “EUR”, or “USD”. In the period of three years (January 2014 to December 2016) almost 15 million tweets were collected. A subset of them (44,000 tweets) was manually labeled by knowledgeable students of finance. The label captures the leaning or stance of the Twitter user with respect to the anticipated move of one currency w.r.t. the other. The stance is represented by three values: buy (EUR vs. USD), hold, or sell. The tweets were collected, labeled and provided to us by the Sowa Labs company (http://www.sowalabs.com). Tweets related to Forex, specifically to EUR and USD, were acquired through the Twitter search API with the following query: “EURUSD”, “USDEUR”, “EUR”, or “USD”. In the period of three years (January 2014 to December 2016) almost 15 million tweets were collected. A subset of them (44,000 tweets) was manually labeled by knowledgeable students of finance.

How many tweets were manually labelled?

The answers are shown as follows: * 44,000 tweets

Tell me whether these are items used for caring for a baby or a dog: crate, diaper, leash, swaddle, and chew toy.

The diaper and swaddle are for a baby, and the crate and leash are for a dog. Both babies and dogs use chew toys.

Why do some countries call football soccer?

Football started in England and was governed by the Football Association. The term Soccer was used at an abbreviation of the the word Association to help distinguish it from Rugby.

Classify each of the following as edible or not edible: hamburger, toast, rubber, leather, pizza, plastic

Hamburger: Edible Toast: Edible Rubber: Not Edible Leather: Not Edible Pizza: Edible Plastic: Not Edible

Long before any knowledge of electricity existed, people were aware of shocks from electric fish. Ancient Egyptian texts dating from 2750 BCE referred to these fish as the "Thunderer of the Nile", and described them as the "protectors" of all other fish. Electric fish were again reported millennia later by ancient Greek, Roman and Arabic naturalists and physicians. Several ancient writers, such as Pliny the Elder and Scribonius Largus, attested to the numbing effect of electric shocks delivered by electric catfish and electric rays, and knew that such shocks could travel along conducting objects. Patients with ailments such as gout or headache were directed to touch electric fish in the hope that the powerful jolt might cure them. Ancient cultures around the Mediterranean knew that certain objects, such as rods of amber, could be rubbed with cat's fur to attract light objects like feathers. Thales of Miletus made a series of observations on static electricity around 600 BCE, from which he believed that friction rendered amber magnetic, in contrast to minerals such as magnetite, which needed no rubbing. Thales was incorrect in believing the attraction was due to a magnetic effect, but later science would prove a link between magnetism and electricity. According to a controversial theory, the Parthians may have had knowledge of electroplating, based on the 1936 discovery of the Baghdad Battery, which resembles a galvanic cell, though it is uncertain whether the artifact was electrical in nature. Electricity would remain little more than an intellectual curiosity for millennia until 1600, when the English scientist William Gilbert wrote De Magnete, in which he made a careful study of electricity and magnetism, distinguishing the lodestone effect from static electricity produced by rubbing amber. He coined the New Latin word electricus ("of amber" or "like amber",, elektron, the Greek word for "amber") to refer to the property of attracting small objects after being rubbed. This association gave rise to the English words "electric" and "electricity", which made their first appearance in print in Thomas Browne's Pseudodoxia Epidemica of 1646. Further work was conducted in the 17th and early 18th centuries by Otto von Guericke, Robert Boyle, Stephen Gray and C. F. du Fay. Later in the 18th century, Benjamin Franklin conducted extensive research in electricity, selling his possessions to fund his work. In June 1752 he is reputed to have attached a metal key to the bottom of a dampened kite string and flown the kite in a storm-threatened sky. A succession of sparks jumping from the key to the back of his hand showed that lightning was indeed electrical in nature. He also explained the apparently paradoxical behavior of the Leyden jar as a device for storing large amounts of electrical charge in terms of electricity consisting of both positive and negative charges In 1775, Hugh Williamson reported a series of experiments to the Royal Society on the shocks delivered by the electric eel; that same year the surgeon and anatomist John Hunter described the structure of the fish's electric organs. In 1791, Luigi Galvani published his discovery of bioelectromagnetics, demonstrating that electricity was the medium by which neurons passed signals to the muscles. Alessandro Volta's battery, or voltaic pile, of 1800, made from alternating layers of zinc and copper, provided scientists with a more reliable source of electrical energy than the electrostatic machines previously used. The recognition of electromagnetism, the unity of electric and magnetic phenomena, is due to Hans Christian Ørsted and André-Marie Ampère in 1819–1820. Michael Faraday invented the electric motor in 1821, and Georg Ohm mathematically analysed the electrical circuit in 1827. Electricity and magnetism (and light) were definitively linked by James Clerk Maxwell, in particular in his "On Physical Lines of Force" in 1861 and 1862.  While the early 19th century had seen rapid progress in electrical science, the late 19th century would see the greatest progress in electrical engineering. Through such people as Alexander Graham Bell, Ottó Bláthy, Thomas Edison, Galileo Ferraris, Oliver Heaviside, Ányos Jedlik, William Thomson, 1st Baron Kelvin, Charles Algernon Parsons, Werner von Siemens, Joseph Swan, Reginald Fessenden, Nikola Tesla and George Westinghouse, electricity turned from a scientific curiosity into an essential tool for modern life. In 1887, Heinrich Hertz discovered that electrodes illuminated with ultraviolet light create electric sparks more easily. In 1905, Albert Einstein published a paper that explained experimental data from the photoelectric effect as being the result of light energy being carried in discrete quantized packets, energising electrons. This discovery led to the quantum revolution. Einstein was awarded the Nobel Prize in Physics in 1921 for "his discovery of the law of the photoelectric effect". The photoelectric effect is also employed in photocells such as can be found in solar panels. The first solid-state device was the "cat's-whisker detector" first used in the 1900s in radio receivers. A whisker-like wire is placed lightly in contact with a solid crystal (such as a germanium crystal) to detect a radio signal by the contact junction effect. In a solid-state component, the current is confined to solid elements and compounds engineered specifically to switch and amplify it. Current flow can be understood in two forms: as negatively charged electrons, and as positively charged electron deficiencies called holes. These charges and holes are understood in terms of quantum physics. The building material is most often a crystalline semiconductor. Solid-state electronics came into its own with the emergence of transistor technology. The first working transistor, a germanium-based point-contact transistor, was invented by John Bardeen and Walter Houser Brattain at Bell Labs in 1947, followed by the bipolar junction transistor in 1948.

Extract from the following passage research related to electricity in the 17th and 18th centuries.

Further work was conducted in the 17th and early 18th centuries by Otto von Guericke, Robert Boyle, Stephen Gray and C. F. du Fay. Later in the 18th century, Benjamin Franklin conducted extensive research in electricity, selling his possessions to fund his work. In June 1752 he is reputed to have attached a metal key to the bottom of a dampened kite string and flown the kite in a storm-threatened sky. A succession of sparks jumping from the key to the back of his hand showed that lightning was indeed electrical in nature. He also explained the apparently paradoxical behavior of the Leyden jar as a device for storing large amounts of electrical charge in terms of electricity consisting of both positive and negative charges In 1775, Hugh Williamson reported a series of experiments to the Royal Society on the shocks delivered by the electric eel; that same year the surgeon and anatomist John Hunter described the structure of the fish's electric organs. In 1791, Luigi Galvani published his discovery of bioelectromagnetics, demonstrating that electricity was the medium by which neurons passed signals to the muscles. Alessandro Volta's battery, or voltaic pile, of 1800, made from alternating layers of zinc and copper, provided scientists with a more reliable source of electrical energy than the electrostatic machines previously used.

Identify which instrument is string or percussion: Rock gong, Yazheng

Yazheng is string, Rock gong is percussion.

We have extracted the bio-medical entities present in the Wikipedia medical articles through a n-gram-based technique. A pre-processing phase occurs in a similar way as for the dictionary composition. Given a Wikipedia article written in English, we have pre-processed the textual part through the Tanl pipeline. Similar to what described in Section "Reference dictionary" for the reference dictionary, we have first divided the text in sentences and the sentences into single word forms. For each form, we have considered the lemma (when available) and the part of speech (POS). For instance, starting from an example sentence extracted from the Wikipedia page on the Alzheimer's disease: “Other risk factors include a history of head injuries, depression, or hypertension.", we have obtained the annotation shown in Figure 4 . As in the case of the dictionary, each word in the text has been lowercasing. After pre-processing the text of each article, we have attempted to match each n-gram (with n between 1 and 10) in the corpus with the entries in the extended dictionary. We both attempt an exact match and an approximate match, the latter removing prepositions, punctuations and articles from the n-grams. Approximate matching leads to several advantages. Indeed, exploiting the text pre-processing allows to identify dictionary definitions present in the text, even when the number differs. As an example, the dictionary definition “injury" will match with “injuries", mentioned in the text, because in the approximation one can consider the lemmas. Further, considering the POS allows to identify mentions when interleaved by prepositions, articles, and conjunctions that change the form but do not alter the meaning. As an example, the approximate definition “aneurysm vein galen” will match also with the following n-gram: “the aneurysm and vein of galen", if present in the text. We have extracted the bio-medical entities present in the Wikipedia medical articles through a n-gram-based technique.

How to extract the biomedical entities present in the Wikipedia medical articles in this paper?

The authors extracted the bio-medical entities through a n-gram-based technique.

We developed an interface to perform the manual evaluation of the retrieved answers. Figure 5 presents the evaluation interface showing, for each test question, the top-10 answers of the evaluated QA method and the reference answer(s) used by LiveQA assessors to help judging the retrieved answers by the participating systems. We used the test questions of the medical task at TREC-2017 LiveQA BIBREF11 . These questions are randomly selected from the consumer health questions that the NLM receives daily from all over the world. The test questions cover different medical entities and have a wide list of question types such as Comparison, Diagnosis, Ingredient, Side effects and Tapering. For a relevant comparison, we used the same judgment scores as the LiveQA Track: Correct and Complete Answer (4) Correct but Incomplete (3) Incorrect but Related (2) Incorrect (1) We evaluated the answers returned by the IR-based method and the hybrid QA method (IR+RQE) according to the same reference answers used in LiveQA-Med. The answers were anonymized (the method names were blinded) and presented to 3 assessors: a medical doctor (Assessor A), a medical librarian (B) and a researcher in medical informatics (C). None of the assessors participated in the development of the QA methods. Assessors B and C evaluated 1,000 answers retrieved by each of the methods (IR and IR+RQE). Assessor A evaluated 2,000 answers from both methods. Table TABREF103 presents the inter-annotator agreement (IAA) through F1 score computed by considering one of the assessors as reference. In the first evaluation, we computed the True Positives (TP) and False Positives (FP) over all ratings and the Precision and F1 score. As there are no negative labels (only true or false positives for each category), Recall is 100%. We also computed a partial IAA by grouping the "Correct and Complete Answer" and "Correct but Incomplete" ratings (as Correct), and the "Incorrect but Related" and "Incorrect" ratings (as Incorrect). The average agreement on distinguishing the Correct and Incorrect answers is 94.33% F1 score. Therefore, we used the evaluations performed by assessor A for both methods. The official results of the TREC LiveQA track relied on one assessor per question as well. We evaluated the answers returned by the IR-based method and the hybrid QA method (IR+RQE) according to the same reference answers used in LiveQA-Med.

How to evaluate the answers returned by the IR-based method and the hybrid QA method (IR+RQE)?

The authors evaluated the answers returned by the IR-based method and the hybrid QA method (IR+RQE) according to the same reference answers used in LiveQA-Med.

Why did the cow jump over the moon?

The cow wanted to jump to the moon because it had run out of milk for its calf. The calf was crying day and night because of how hungry it was. The mother cow looked up at the night sky, saw the milky-colored moon in the sky, and thought if it jumped to the moon, its udders would be full again. It practiced over and over, jumping as high as it could. Once it was finally ready that it jumped over the moon. The courage that it gained filled the udders with milk once again.

Main branches of engineering For a topical guide, see Outline of engineering. Hoover Dam Engineering is a broad discipline that is often broken down into several sub-disciplines. Although an engineer will usually be trained in a specific discipline, he or she may become multi-disciplined through experience. Engineering is often characterized as having four main branches: chemical engineering, civil engineering, electrical engineering, and mechanical engineering. Chemical engineering Main article: Chemical engineering Chemical engineering is the application of physics, chemistry, biology, and engineering principles in order to carry out chemical processes on a commercial scale, such as the manufacture of commodity chemicals, specialty chemicals, petroleum refining, microfabrication, fermentation, and biomolecule production. Civil engineering Main article: Civil engineering Civil engineering is the design and construction of public and private works, such as infrastructure (airports, roads, railways, water supply, and treatment etc.), bridges, tunnels, dams, and buildings. Civil engineering is traditionally broken into a number of sub-disciplines, including structural engineering, environmental engineering, and surveying. It is traditionally considered to be separate from military engineering. Electrical engineering Main article: Electrical engineering Electric motor Electrical engineering is the design, study, and manufacture of various electrical and electronic systems, such as broadcast engineering, electrical circuits, generators, motors, electromagnetic/electromechanical devices, electronic devices, electronic circuits, optical fibers, optoelectronic devices, computer systems, telecommunications, instrumentation, control systems, and electronics. Mechanical engineering Main article: Mechanical engineering Mechanical engineering is the design and manufacture of physical or mechanical systems, such as power and energy systems, aerospace/aircraft products, weapon systems, transportation products, engines, compressors, powertrains, kinematic chains, vacuum technology, vibration isolation equipment, manufacturing, robotics, turbines, audio equipments, and mechatronics. Bioengineering Main article: Biological engineering Bioengineering is the engineering of biological systems for a useful purpose. Examples of bioengineering research include bacteria engineered to produce chemicals, new medical imaging technology, portable and rapid disease diagnostic devices, prosthetics, biopharmaceuticals, and tissue-engineered organs.

Please give me a short bulleted list of the principal fields of engineering

chemical engineering, civil engineering, electrical engineering, mechanical engineering and bioengineering

The Airbus A380 is a large wide-body airliner that was developed and produced by Airbus. It is the world's largest passenger airliner and only full-length double-deck jet airliner. Airbus studies started in 1988, and the project was announced in 1990 to challenge the dominance of the Boeing 747 in the long-haul market. The then-designated A3XX project was presented in 1994; Airbus launched the €9.5 billion ($10.7 billion) A380 programme on 19 December 2000. The first prototype was unveiled in Toulouse on 18 January 2005, with its first flight on 27 April 2005. It then obtained its type certificate from the European Aviation Safety Agency (EASA) and the US Federal Aviation Administration (FAA) on 12 December 2006. Due to difficulties with the electrical wiring, the initial production was delayed by two years and the development costs almost doubled. It was first delivered to Singapore Airlines on 15 October 2007 and entered service on 25 October. Production peaked at 30 per year in 2012 and 2014. However, after the largest customer, Emirates, reduced its last order in February 2019, Airbus announced that A380 production would end in 2021. On 16 December 2021, Emirates received its 123rd A380, which was the 251st and last delivered by Airbus. The $25 billion investment was not recouped. The full-length double-deck aircraft has a typical seating for 525 passengers, with a maximum certified capacity for 853 passengers. The quadjet is powered by Engine Alliance GP7200 or Rolls-Royce Trent 900 turbofans providing a range of 8,000 nmi (14,800 km). As of December 2021, the global A380 fleet had completed more than 800,000 flights over 7.3 million block hours with no fatalities and no hull losses. As of December 2022, there were 237 aircraft in service with 16 operators worldwide.

What is the biggest passenger airliner?

A380 is the largest passenger airliner. It has a typical seating for 525 passengers, with a maximum certified capacity for 853 passengers and has a range of 14,800km.

What is the most popular sport?

Currently soccer/football is the most popular sport globally to participate in and watch with an estimated 3.5 billion fans. With the low entry cost to play and ability to be played or practiced most anywhere it makes it easy for pretty much anyone to participate.

Classify each of the following as either transportation modes or brands of vehicles: Audi, Bus, Train, Airplane, Ship, Ford, Acura, Volkswagen.

The following are transportation modes: Bus, Train, Airplane, Ship. The following are brands of vehicles: Audit, Ford, Acura, Volkswagen.

What is Depreciation

Depreciation is the drop in value of an asset due to wear and tear, age and obsolescence (going out of date) as recorded in an organization's financial records.

Write a friendly complaint to an airline service about a poor experience with multiple issues and ask for points.

To whom it may concern; as a loyal member to your airline alliance, I was disappointed in the multiple issues I experienced on a recent flight. Your airline usually provides me with an exceptional experience but unfortunately this last travel journey was below par. Firstly, my seat was broken and unable to recline. Given this was a red eye flight where I was hoping to get some rest, this limited my ability to do so. Secondly, the Wifi I paid for was out for 4 out of 6 hours of the trip. So while awake without recline, I was unable to use that time effectively to email or communicate with friends and family. Lastly, my checked bag took 45 minutes at the carousel. I know you have a policy where it should be 20 minutes or less. I really enjoy flying with you and would like to hope this was a stroke of bad luck, and not reflective of your customer service and typical experience. I would appreciate as a goodwill 10,000 points into my account to make things right. I hope you consider my request. Kind regards

What is the difference between Hunter, Jumper, and Equitation horseback riding?

The biggest difference between Hunter, Jumper, and Equitation horseback riding is the way they are judged. Hunters are scored by the way the horse looks, Jumpers are scored on time and the number of faults over jumps, and Equitation is scored on the performance and style of the rider.

What is Remote Sensing?

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about Earth and other planets. Remote sensing is used in numerous fields, including geophysics, geography, land surveying and most Earth science disciplines (e.g. exploration geophysics, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others. In current usage, the term remote sensing generally refers to the use of satellite- or aircraft-based sensor technologies to detect and classify objects on Earth. It includes the surface and the atmosphere and oceans, based on propagated signals (e.g. electromagnetic radiation). It may be split into "active" remote sensing (when a signal is emitted by a satellite or aircraft to the object and its reflection detected by the sensor) and "passive" remote sensing (when the reflection of sunlight is detected by the sensor).

We compared our model with MLE, RL and GAN baselines. Since COCO and EMNLP2017 WMT don't have input while WeiboDial regards posts as input, we chose the following baselines respectively: MLE: a RNN model trained with MLE objective BIBREF4 . Its extension, Seq2Seq, can work on the dialogue dataset BIBREF2 . SeqGAN: The first text GAN model that updates the generator with policy gradient based on the rewards from the discriminator BIBREF7 . LeakGAN: A variant of SeqGAN that provides rewards based on the leaked information of the discriminator for the generator BIBREF11 . MaliGAN: A variant of SeqGAN that optimizes the generator with a normalized maximum likelihood objective BIBREF8 . IRL: This inverse reinforcement learning method replaces the discriminator with a reward approximator to provide dense rewards BIBREF12 . RAML: A RL approach to incorporate MLE objective into RL training framework, which regards BLEU as rewards BIBREF17 . DialogGAN: An extension of SeqGAN tuned to dialogue generation task with MLE objective added to the adversarial objective BIBREF16 . DPGAN: A variant of DialogGAN which uses a language model based discriminator and regards cross-entropy as rewards BIBREF13 . Note that MLE, SeqGAN, LeakGAN, MaliGAN and IRL are the baselines on COCO and EMNLP2017 WMT, while MLE, RAML, DialogGAN, and DPGAN on WeiboDial. The original codes are used to test the baselines. We compared our model with MLE, RL and GAN baselines.

What are baselines in this paper?

MLE, RL and GAN baselines.

During Passover, what are some distinctions of what is and is not Kosher?

Typically, there are two main branches of Judaism based on geography. Ashkenazy, predominately from Europe, and Sephardic, non-European. Overall grain is prohibited and many other items they may be unholy for the 8 day period. However, within the Sephardic tradition, certain allowances are made that are not observed by Ashkenazy, such as beans and corn. In some practices, dependent on the degree of religious practice, the observant will share the tradition of the community in which they reside rather than the geographical definition.

Categorize actions that would be effective in the event of a major earthquake as either 'effective' or 'not effective’. A. Check information via radio. B. Secure an evaculation route. C. Stay wherever you originally are. D. Use a car. E. Turn off a fire when you’re cooking.

A. Check information via radio., B. Secure an evaculation route., and E. Turn off a fire when you’re cooking. are effective while C. Stay wherever you originally are. and D. Use a car. are not effective.

Write a plan for a road trip to ski resorts in California that are available on the Ikon pass, with stops at three different resorts. The plan should depart and end at San Francisco, include a budget, timeline, and short description of each resort.

On day 1, start from San Francisco and drive to Palisades Tahoe, which is a roughly 200mi drive which will take around 3 hours. Palisades Tahoe, which once hosted the 1960 winter Olympics, offers two expansive mountains with a variety of beginner to advanced terrain. Finish off day 1 by driving 176mi or around 4 hours to a hotel nearby the next resort, Mammoth Mountain. On day 2, spend the day skiing at Mammoth Mountain, which boasts high snowfalls, is home to the highest elevation lift in California, and has some of the greatest views of the Sierra Nevada mountains. Finish off day 2 by driving 314mi or around 5.5 hours to a hotel nearby the last resort, Big Bear Mountain. On day 3, you will ski Big Bear Mountain Resort comprised of Bear Mountain, with its park-heavy terrain, and Snow Summit, with its many beginner-friendly trails. Big Bear Mountain Resort is not far from Los Angeles and San Bernardino, so additional days could be spent exploring those metro areas. Spend the night around Big Bear Mountain Resort. On the last day, make the nearly 500mi or 7-8 hour drive back to San Francisco. Across this 4 day trip, you can expect to spend around $800 on gas costs and around $600-$800 on lodging with 1 night around Mammoth Mountain and 2 nights around Big Bear Mountain Resort.

The Roman Forum, also known by its Latin name Forum Romanum (Italian: Foro Romano), is a rectangular forum (plaza) surrounded by the ruins of several important ancient government buildings at the center of the city of Rome. Citizens of the ancient city referred to this space, originally a marketplace, as the Forum Magnum, or simply the Forum. For centuries the Forum was the center of day-to-day life in Rome: the site of triumphal processions and elections; the venue for public speeches, criminal trials, and gladiatorial matches; and the nucleus of commercial affairs. Here statues and monuments commemorated the city's great men. The teeming heart of ancient Rome, it has been called the most celebrated meeting place in the world, and in all history. Located in the small valley between the Palatine and Capitoline Hills, the Forum today is a sprawling ruin of architectural fragments and intermittent archaeological excavations attracting 4.5 million or more sightseers yearly.

Why is the forum in Rome named the forum?

The Forum in Rome is the location of the ruins of the ancient city of Rome. The forum is derived from Latin, meaning rectangular plaza. The plaza was the center of activity in Rome. Staging speeches trials and public gatherings.

Negotiations, either between individuals or entities, are ubiquitous in everyday human interactions ranging from sales to legal proceedings. Being a good negotiator is a complex skill, requiring the ability to understand the partner's motives, ability to reason and to communicate effectively, making it a challenging task for an automated system. While research in building automatically negotiating agents has primarily focused on agent-agent negotiations BIBREF0, BIBREF1, there is a recent interest in agent-human negotiations BIBREF2 as well. Such agents may act as mediators or can be helpful for pedagogical purposes BIBREF3. Efforts in agent-human negotiations involving free-form natural language as a means of communication are rather sparse. Researchers BIBREF4 recently studied natural language negotiations in buyer-seller bargaining setup, which is comparatively less restricted than previously studied game environments BIBREF5, BIBREF6. Lack of a well-defined structure in such negotiations allows humans or agents to express themselves more freely, which better emulates a realistic scenario. Interestingly, this also provides an exciting research opportunity: how can an agent leverage the behavioral cues in natural language to direct its negotiation strategies? Understanding the impact of natural language on negotiation outcomes through a data-driven neural framework is the primary objective of this work. We focus on buyer-seller negotiations BIBREF4 where two individuals negotiate the price of a given product. Leveraging the recent advancements BIBREF7, BIBREF8 in pre-trained language encoders, we attempt to predict negotiation outcomes early on in the conversation, in a completely data-driven manner (Figure FIGREF3). Early prediction of outcomes is essential for effective planning of an automatically negotiating agent. Although there have been attempts to gain insights into negotiations BIBREF9, BIBREF10, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section SECREF3). Our evaluations show that natural language allows the models to make better predictions by looking at only a fraction of the negotiation. Rather than just realizing the strategy in natural language, our empirical results suggest that language can be crucial in the planning as well. We provide a sample negotiation from the test set BIBREF4 along with our model predictions in Table TABREF1. Although there have been attempts to gain insights into negotiations, to the best of our knowledge, we are the first to study early natural language cues through a data-driven neural system (Section 2).

Are the authors the first to study early natural language cues through a data-driven?

Yes, they are.

The Call to Adventure The hero begins in a situation of normality from which some information is received that acts as a call to head off into the unknown. According to Campbell, this region is represented by a distant land, a forest, a kingdom underground, beneath the waves, or above the sky, a secret island, lofty mountaintop, or profound dream state; but it is always a place of strangely fluid and polymorphous beings, unimaginable torments, superhuman deeds, and impossible delight. The hero can go forth of their own volition to accomplish the adventure, as did Theseus when he arrived in his father's city, Athens, and heard the horrible history of the Minotaur; or they may be carried or sent abroad by some benign or malignant agent as was Odysseus, driven about the Mediterranean by the winds of the angered god, Poseidon. The adventure may begin as a mere blunder... or still, again, one may be only casually strolling when some passing phenomenon catches the wandering eye and lures one away from the frequented paths of man. Examples might be multiplied, ad infinitum, from every corner of the world.

What is one of Poseidon's many powers?

Poseidon has power to control the winds that guide ships on the seas of the Mediterranean.

Where to visit in Brazil?

It depends what you are looking for. Brazil is known for the amazon forest, carnival and beaches in Rio but there are a lot more to explore. There are pretty interesting cities, such as São Paulo, Curitiba and Recife. São Paulo has a great mix of culture and gastronomy, the city is vibrant and have bakeries that stay up all night. Curitiba became a leader in urban sustainability and also has a cultural aspect to it. Recife is a city in the northeastern state of Pernambuco, it has a night life in its old town, Recife antigo and some beaches as well. During the month of February the city has a vibrant carnival celebration, tourists and residents go out to the street to dance and celebrate, where you can see people of all ages. Besides the cities, Brazil has beautiful warm beaches in its northeastern states. From the Bahia state to Ceára state, there are many paradisiac beaches to visit. Be aware that road tripping in Brazil is not always safe and you need to speak Portuguese since you will find English speakers only in the main hotels and resorts. Stay in the main cities of Salvador, Aracajú, Maceió, Recife, João Pessoa, Natal and Fortaleza to experience the most development and tourist services.

What is a term deposit?

With a term deposit, you lock away an amount of money for an agreed length of time (the 'term') – that means you can't access the money until the term is up. In return, you'll get a guaranteed rate of interest for the term you select, so you'll know exactly what the return on your money will be.

Why kids who pursue athletics do well in professional life ?

Study shows that kids who pursue athletics are on average more successful in their professional life. This can largely attributed to traits such as grit, practice, competitiveness, teamwork, passion and drive that athletics fosters in participating kids. These traits continue to serve well as individuals grow into their professional life. -

The Great Barrier Reef Marine Park Authority considers the greatest threat to the Great Barrier Reef to be climate change, causing ocean warming which increases coral bleaching. Mass coral bleaching events due to marine heatwaves occurred in the summers of 1998, 2002, 2006, 2016, 2017 and 2020, and coral bleaching is expected to become an annual occurrence. In 2020, a study found that the Great Barrier Reef has lost more than half of its corals since 1995 due to warmer seas driven by climate change. As global warming continues, corals will not be able to keep up with increasing ocean temperatures. Coral bleaching events lead to increased disease susceptibility, which causes detrimental ecological effects for reef communities.

What is the biggest threat to the great barrier reef?

Climate change is the biggest threat to the Great Barrier Reef. Major heat waves over the past 30 years have increased coral bleaching and is now expected to occur yearly. Coral bleaching increases the likelihood of disease and is therefore destroying reef communities.

How many voyages did Columbus lead to America?

Four

1703.02507

We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings. Conceptually, the model can be interpreted as a natural extension of the word-contexts from C-BOW BIBREF0 , BIBREF1 to a larger sentence context, with the sentence words being specifically optimized towards additive combination over the sentence, by means of the unsupervised objective function. Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0 We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0

How do the n-gram features incorporate compositionality?

The answers are shown as follows: * by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words

1708.05873

We use a new dataset of GD statements from 1970 to 2016, the UN General Debate Corpus (UNGDC), to examine the international development agenda in the UN BIBREF3 . Our application of NLP to these statements focuses in particular on structural topic models (STMs) BIBREF4 . The paper makes two contributions using this approach: (1) It sheds light on the main international development issues that governments prioritise in the UN; and (2) It identifies the key country-specific factors associated with governments discussing development issues in their GD statements. FLOAT SELECTED: Fig. 2. Topic quality. 20 highest probability words for the 16-topic model. We use a new dataset of GD statements from 1970 to 2016, the UN General Debate Corpus (UNGDC), to examine the international development agenda in the UN BIBREF3 . FLOAT SELECTED: Fig. 2. Topic quality. 20 highest probability words for the 16-topic model.

Is the dataset multilingual?

No.

1611.02550

Our classifier-based embeddings use LSTM or GRU networks with 2–4 stacked layers and 1–3 fully connected layers. The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061. The recurrent hidden state dimensionality is fixed at 512 and dropout BIBREF32 between stacked recurrent layers is used with probability INLINEFORM0 . The fully connected hidden layer dimensionality is fixed at 1024. Rectified linear unit (ReLU) non-linearities and dropout with INLINEFORM1 are used between fully-connected layers. However, between the final recurrent hidden state output and the first fully-connected layer no non-linearity or dropout is applied. These settings were determined through experiments on the development set. The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.

Which dimensionality do they use for their embeddings?

The answers are shown as follows: * 1061

What kind of method is Transfer printing

Transfer printing is a method of decorating pottery or other materials using an engraved copper or steel plate from which a monochrome print on paper is taken which is then transferred by pressing onto the ceramic piece. Pottery decorated using the technique is known as transferware or transfer ware. It was developed in England from the 1750s on, and in the 19th century became enormously popular in England, though relatively little used in other major pottery-producing countries. The bulk of production was from the dominant Staffordshire pottery industry. America was a major market for English transfer-printed wares, whose imagery was adapted to the American market; several makers made this almost exclusively. The technique was essential for adding complex decoration such as the Willow pattern to relatively cheap pottery. In particular, transfer printing brought the price of a matching dinner service low enough for large numbers of people to afford. Apart from pottery, the technique was used on metal, and enamelled metal, and sometimes on wood and textiles. It remains used today, although mostly superseded by lithography. In the 19th century methods of transfer printing in colour were developed.

Explain me the concept of "Karma" in Buddhism.

Karma is one of the most misunderstood aspects of Buddhism. In simple terms, Karma is the law of action and reactions. Our actions, both physical and thoughts, are the acts that are put in the world. Reactions can be referred to as their manifestations in the actor's life, i.e. our own life. For example, suppose your mind is filled with greed, evil thoughts like harming another person, and jealousy. In that case, it reflects restlessness as we will never be satisfied, punishments for harming another person, and stress to become like someone else, respectively. For a positive example, a life lived with generosity, righteousness, kindness, and genuinely putting good out there will lead to respect in society and a fulfilled life. Furthermore, Buddhism believes in reincarnation. So the good acts and deeds that one puts in this life, one also reaps the benefits in the next life. Therefore, the equation of Karma is relatively straightforward. Do good and work for the betterment of society and oneself.

Classify the following games in ascending order of computational complexity: tic-tac-toe, chess, checkers, go

tic-tac-toe, checkers, chess, go

Why is standard guitar tuning EADGBE?

The standard guitar tuning of EADGBE offers consistency and convenience. The interval between each successive pair of strings is a perfect fourth, except the major third interval between G and B, making some figures and phrases 'translate' horizontally across the neck. The perfect fourth interval helps avoid long finger stretches in comparison to the perfect fifth intervals on smaller instruments like the violin. The introduction of a major third between G and B means that the total interval between 1st and 6th string is 24 semitones, or 2 octaves, making the open guitar strings fall more easily into chords, aiding fretting of basic chords with fewer fingers.