DataHammer/scimrc huggingface dataset:
Question: Suggest some brass instruments I can learn if I want to play in an orchestra?
Answer: French Horn, Trombone, Trumpet and Tuba are common brass instruments in orchestras.
Question: Can you provide how to split target data to multiple devices in the appendix?
Evidence: In the first group of experiments, M is set to 5, and the target data are randomly partitioned into 5 parts each of which is held by one edge device.
Answer: In our experiments of multiple clients, the target data are randomly divided into five parts.
Question: Identify which instrument is string or percussion: Bell plate, Hululu
Answer: Hululu is string, Bell plate is percussion.
Question: What is the difference between aerobic and anaerobic exercise?
Answer: The difference between aerobic and anaerobic exercise is whether or not the body breaks down glucose with or without oxygen.
Question: Have the following eye-movement behaviors been considered as features in the model: forward saccades, skips, and regressions?
Evidence: Readers' eye-movement behavior, characterized by fixations, forward saccades, skips and regressions, can be directly quantified by simple statistical aggregation (i.e., either computing features for individual participants and then averaging or performing a multi-instance based learning as explained in section SECREF6 ). Since these eye-movement attributes relate to the cognitive process in reading BIBREF17 , we consider these as features in our model. Some of these features have been reported by sarcasmunderstandability for modeling sarcasm understandability of readers. However, as far as we know, these features are being introduced in NLP tasks like textual sarcasm detection for the first time. The values of these features are believed to increase with the increase in the degree of surprisal caused by incongruity in text (except skip count, which will decrease).
Readers’ eye-movement behavior, characterized by fixations, forward saccades, skips and regressions, can be directly quantified by simple statistical aggregation (i.e., either computing features for individual participants and then averaging or performing a multi-instance based learning as ex plained in section 6). Since these eye-movement attributes relate to the cognitive process in reading (Rayner and Sereno, 1994), we consider these as features in our model.
Answer: Yes.
Question: Describe a tense scene of a dystopian, futuristic, ritualistic gathering of hooded and masked individuals discussing the evolving threat of intelligent machines.
Answer: In a dimly lit, futuristic circular room with a concrete table in the center, a group of masked individuals wearing robes with the high council members are standing around a large circular table on an elevated platform conferring. The members are wearing hooded cloaks and various masks, such as skull masks, Kitsune masks, and demon masks. The atmosphere is mysterious and tense as the council members lead the discussion. At the head of the table, a woman named Quo is standing and addressing the group, while several male and female masked elders listen intently.

In the background, thousands of other masked individuals fill the auditorium, adding to the chaotic atmosphere of the gathering. Quo is passionately making a point about the threat posed by evolving machines, while one of the masked elders is seen leaning in and expressing concern. The scene is filled with tension and emotion, with everyone engaged in a heated argument about the future of their realm and the role of divine strength versus technology.
Question: What was the most expensive watch ever sold at auction
Evidence: The most expensive watch ever sold at auction worldwide is the Patek Philippe Grandmaster Chime Ref. 6300A-010, which fetched 31.19 million US dollars (31,000,000 CHF) in Geneva on 9 November 2019 (by Christie's).
Answer: A Patek Philippe Grandmaster was sold for a reported 31.19 million USD
Question: How many times Daniel Ricciardo won the F1 championship ?
Answer: 0 times
Question: What is an "answer style"?
Evidence: We conducted experiments on the two tasks of MS MARCO 2.1 BIBREF5 . The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. For instance, for the question “tablespoon in cup”, the answer in the Q&A task will be “16”, and the answer in the NLG task will be “There are 16 tablespoons in a cup.” In addition to the ALL dataset, we prepared two subsets (Table 1 ). The ANS set consists of answerable questions, and the WFA set consists of the answerable questions and well-formed answers, where WFA $\subset $ ANS $\subset $ ALL.
The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question.
Answer: well-formed sentences vs concise answers
Question: Do they perform any morphological tokenization?
Evidence: This step entails counting occurrences of all words in the training corpus and sorting them in order of decreasing occurrence. As mentioned, the vocabulary is taken to be the INLINEFORM0 most frequently occurring words, that occur at least some number INLINEFORM1 times. It is implemented in Spark as a straight-forward map-reduce job.
This step entails counting occurrences of all words in the training corpus and sorting them in order of decreasing occurrence. As mentioned, the vocabulary is taken to be the INLINEFORM0 most frequently occurring words, that occur at least some number INLINEFORM1 times. It is implemented in Spark as a straight-forward map-reduce job.
Answer: No.
Question: List the vegetation found in Antarctica in a common separated manner.
Evidence: Antarctica (/ænˈtɑːrktɪkə/) is Earth's southernmost and least-populated continent. Situated almost entirely south of the Antarctic Circle and surrounded by the Southern Ocean (also known as the Antarctic Ocean), it contains the geographic South Pole. Antarctica is the fifth-largest continent, being about 40% larger than Europe, and has an area of 14,200,000 km2 (5,500,000 sq mi). Most of Antarctica is covered by the Antarctic ice sheet, with an average thickness of 1.9 km (1.2 mi).
Antarctica is, on average, the coldest, driest, and windiest of the continents, and it has the highest average elevation. It is mainly a polar desert, with annual precipitation of over 200 mm (8 in) along the coast and far less inland. About 70% of the world's freshwater reserves are frozen in Antarctica, which, if melted, would raise global sea levels by almost 60 metres (200 ft). Antarctica holds the record for the lowest measured temperature on Earth, −89.2 °C (−128.6 °F). The coastal regions can reach temperatures over 10 °C (50 °F) in summer. Native species of animals include mites, nematodes, penguins, seals and tardigrades. Where vegetation occurs, it is mostly in the form of lichen or moss.
The ice shelves of Antarctica were probably first seen in 1820, during a Russian expedition led by Fabian Gottlieb von Bellingshausen and Mikhail Lazarev. The decades that followed saw further exploration in French, American, and British expeditions. The first confirmed landing was by a Norwegian team in 1895. In the early 20th century, there were a few expeditions into the interior of the continent. British explorers were the first to reach the magnetic South Pole in 1909, and the geographic South Pole was first reached in 1911 by Norwegian explorers.
Answer: Lichen, moss.
Question: What netWorks do they use to capture the long-term dependencies and global context information from the whole sentence?
Evidence: Quality estimation (QE) refers to the task of measuring the quality of machine translation (MT) system outputs without reference to the gold translations BIBREF0 , BIBREF1 . QE research has grown increasingly popular due to the improved quality of MT systems, and potential for reductions in post-editing time and the corresponding savings in labor costs BIBREF2 , BIBREF3 . QE can be performed on multiple granularities, including at word level, sentence level, or document level. In this paper, we focus on quality estimation at word level, which is framed as the task of performing binary classification of translated tokens, assigning “OK” or “BAD” labels.
Early work on this problem mainly focused on hand-crafted features with simple regression/classification models BIBREF4 , BIBREF5 . Recent papers have demonstrated that utilizing recurrent neural networks (RNN) can result in large gains in QE performance BIBREF6 . However, these approaches encode the context of the target word by merely concatenating its left and right context words, giving them limited ability to control the interaction between the local context and the target word.
In this paper, we propose a neural architecture, Context Encoding Quality Estimation (CEQE), for better encoding of context in word-level QE. Specifically, we leverage the power of both (1) convolution modules that automatically learn local patterns of surrounding words, and (2) hand-crafted features that allow the model to make more robust predictions in the face of a paucity of labeled data. Moreover, we further utilize stacked recurrent neural networks to capture the long-term dependencies and global context information from the whole sentence.
We tested our model on the official benchmark of the WMT18 word-level QE task. On this task, it achieved highly competitive results, with the best performance over other competitors on English-Czech, English-Latvian (NMT) and English-Latvian (SMT) word-level QE task, and ranking second place on English-German (NMT) and German-English word-level QE task.
Moreover, we further utilize stacked recurrent neural networks to capture the long-term dependencies and global context information from the whole sentence.
Answer: Stacked recurrent neural networks
Question: When I ski, at the end of the turn, I'm not able to get good grip at the end of the turn and the tail washout, why does this happen?
Answer: There are several factors that can contribute to ski not having grip at the end of the turn and the tails washout.

First thing is to make sure your ski edges are sharp, if they are not sharp, you can visit your local ski shop and have the ski shop tune your ski edges.

Once you've made sure your ski edges are sharp, then this might be caused by either incorrect fore-aft balance and/or lateral balance. 

If you are too forward at the end of the turn, meaning you feel more pressure on the ball of your foot, then that will cause the tail of the skis to not be able to bite into the snow and cause it to skid out. At the end of the turn, you want to bring your feet forward and aim to have at least half of the pressure just in front of the heel of the foot, this will allow the edge along the tail of the ski to bite into the snow.

If you have too much pressure on the uphill foot, for example, you are leaning up the hill or transferred your weight too early onto the new downhill foot, that can also cause your current downhill foot to not get enough grip and washout. Try have majority of your weight on your downhill foot until the turn is finished before transferring majority of the weight to the new downhill foot.

It will also be helpful to find a good ski instructor at your local resort or online to look at your skiing and provide feedback.
Question: According to this text, when did Disney become a public company?
Evidence: Disney is one of the biggest and best-known companies in the world, and has been ranked number 53 on the 2022 Fortune 500 list of biggest companies in the United States by revenue. Since its founding, the company has won 135 Academy Awards, 26 of which have been awarded to Walt. The company has been said to have produced some of the greatest films of all time, as well as revolutionizing the theme park industry. Disney has been criticized for supposed plagiarism, depicting racial stereotypes in the past, and both including and lacking LGBT-related elements in its films. The company, which has been public since 1940, trades on the New York Stock Exchange (NYSE) with ticker symbol DIS and has been a component of the Dow Jones Industrial Average since 1991. In August 2020, just under two-thirds of the stock was owned by large financial institutions.
Answer: According to this text, Disney became a publicly listed company on the New York Stock Exchange (NYSE) in 1940 with the ticker symbol DIS.
Question: Why was peanut butter invented?
Answer: Peanut butter was first invented by the Inca, but made a reappearance in North America due to its rediscovery by John Harvey Kellogg in 1895. Peanut butter was made with the purpose of helping older people who couldn’t chew solid foods gain nutrients such as protein. Although it made a reapperance in 1895, it was sold to the public in 1904.
Question: Why do people like the band 'My Morning Jacket?'
Answer: Many committed fans gravitate to their unique sound including traditional rock n roll elements like guitar, drums, bass guitar, and keyboard, but also including unique sounds like slide guitar, saxophone, acoustic guitar, and an Omnichord.  My Morning Jacket's lead singer, guitarist, and songwriter, Jim James, has an incredible voice and is an amazing live performer.  Further, many dedicated fans love their unique live performances and will travel around the world to attend their concerts.
Question: When does daylight savings start and end in Sydney?
Answer: Sydney is in New South Wales (NSW), Australia, and NSW participates in daylight savings. Daylight savings starts at 2am on the first Sunday of October; and ends at 3am on the first Sunday in April

In 2023:
- Daylight savings ends at 3am on Sun 2 April
- Daylight savings starts at 2am on Sun 1 October

Next year, in 2024:
- Daylight savings ends at 3am on Sun 7 April
- Daylight savings starts at 2am on Sun 6 October
Question: As per the passage which schools of Hinduism discuss Ishvara?
Evidence: Another Hindu term that is sometimes translated as deity is Ishvara, or alternatively various deities are described, state Sorajjakool et al., as "the personifications of various aspects of one and the same Ishvara". The term Ishvara has a wide range of meanings that depend on the era and the school of Hinduism. In ancient texts of Indian philosophy, Ishvara means supreme Self, Brahman (Highest Reality), ruler, king or husband depending on the context. In medieval era texts, Ishvara means God, Supreme Being, personal god, or special Self depending on the school of Hinduism.

Among the six systems of Hindu philosophy, Samkhya and Mimamsa do not consider the concept of Ishvara, i.e., a supreme being, relevant. Yoga, Vaisheshika, Vedanta and Nyaya schools of Hinduism discuss Ishvara, but assign different meanings.

Early Nyaya school scholars considered the hypothesis of a deity as a creator God with the power to grant blessings, boons and fruits; but these early Nyaya scholars then rejected this hypothesis, and were non-theistic or atheists. Later scholars of Nyaya school reconsidered this question and offered counter arguments for what is Ishvara and various arguments to prove the existence of omniscient, omnipresent, omnipotent deity (God).
Answer: Yoga, Vaisheshika, Vedanta and Nyaya.
Question: Given the following paragraph about the observation deck of the Burj Khalifa, what's offered on the 124th floor observation deck?
Evidence: An outdoor observation deck, named At the Top, opened on 5 January 2010 on the 124th floor. At 452 m (1,483 ft), it was the highest outdoor observation deck in the world when it opened. Although it was surpassed in December 2011 by Cloud Top 488 on the Canton Tower, Guangzhou at 488 m (1,601 ft), Burj Khalifa opened the 148th floor SKY level at 555 m (1,821 ft), once again giving it the highest observation deck in the world on 15 October 2014, until the Shanghai Tower opened in June 2016 with an observation deck at a height of 561 metres. The 124th floor observation deck also features the electronic telescope, an augmented reality device developed by Gsmprjct° of Montréal, which allows visitors to view the surrounding landscape in real-time, and to view previously saved images such as those taken at different times of day or under different weather conditions. To reduce the daily rush of sightseers, management allows visitors to purchase tickets in advance for a specific date and time, at a 75% discount on tickets purchased on the spot.
Answer: This observation deck offers an augmented reality electronic telescope which lets visitors view either real-time or pre-recorded images of the surrounding landscape.
Question: How does one prepare for interviews at consulting firms?
Answer: Use case study books that are available to practice the case studies in interviews. Often the business schools will have copies of the case study material. In addition, there will be a leadership assessment that is done using examples of projects executed by the interviewee.  Consulting companies like BCG, Bain and McKinsey conduct assessment around Problem solving, structuring, leadership, and empathy/EQ. In addition, some of the questions may require estimations, understanding graphs and doing rough calculations. Interviewee is expected to diagnose the issue, and likely root cause. While evaluating, it is ok to share your thinking and approach to solving the problem. Practicing with your peers helps a lot.
Question: what is quantum computer?
Evidence: A quantum computer is a computer that exploits quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is still largely experimental and impractical.
Answer: A quantum computer  is a computer use quantum mechanical phenomena to conduction the calculation. The current state of the art is still largely experimental a
Question: Who built the great pyramid of Giza?
Answer: Mainstream archaeologists claim that the great pyramid was built by pharaoh Khufu. There is not much evidence to support that claim given that the Egyptians left no written accounts of the methods and timing of the pyramid construction.

Some scientists have proposed the theory that the great pyramids were actually built by a lost civilization. This theory claims that a cataclysmic event whipped out this civilization at the end of the period known as the Younger Dryas.
Question: What marks the change from prehistory to  history?
Answer: The beginning of writing marks the end of prehistory and the beginning of history. This extraordinary human milestone is thought to have taken place in Sumer and Egypt about 5500 years ago, around 3500 BC. The Sumerians and Egyptians had to keep track of the number of crops and animals they grew because they became more settled and needed a more complex social structure. As technology improved, they also had to keep track of a larger number of crops and animals.  Writing is one of the most extraordinary creations of mankind.
Question: Which artist recorded the album Moonlight Madness?
Evidence: Moonlight Madness is the second studio album by singer Teri DeSario, released in 1979 by Casablanca Records and Filmworks (NBLP-7178). It includes the hit single "Yes, I'm Ready", a duet with K.C. of KC and the Sunshine Band.
The album contains mostly disco music, including "Dancin' in the Streets" and "I'm Ready".
Answer: Singer Teri DeSario recorded Moonlight Madness, tracks on the album are mostly in the disco music category and includes the hit single Yes I’m Ready and Dancin in the Streets. 

The album was first released on sale in 1979 on Casablanca Records and Moonlight Madness was the second studio album recorded by DeSario.
Question: In the series A Song of Ice and Fire, who is the founder of House Thenn?
Answer: Sigorn, son of Styr
Question: Do Harry Potter and Hermione get married?
Answer: No. Hermione Granger ends up marrying Ron Weasley and Harry marries Ron's sister, Ginny.
Question: What is the difference between a hazard and out of bounds on a golf course?
Answer: On a golf course, out of bounds areas are marked with white stakes that denote the property boundary of the course. Hazards are marked with red stakes that denote a penalty area within the property of the course (e.g., water, marsh).

When a ball is hit out of bounds, the golfer is penalized one shot and is required to hit the ball again from the same spot. When a ball it hit in a hazard, the golfer is also penalized one shot, but has the option to hit the next shot from the same spot or from the point where the ball crossed the hazard boundary.
Question: List the ten episode titles from the third season of "Game of Thrones" in broadcast order.
Answer: Valar Dohaeris, Dark Wings, Dark Words, Walk of Punishment, And Now His Watch Is Ended, Kiss by Fire, The Climb, The Bear and the Maiden Fair, Second Sons, The Rains of Castamere, Mhysa
Question: What knowledge bases do they use?
Evidence: We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs. More precisely, our semantic parser corresponds to a probabilistic generative model that assigns high probability to sentence semantic parses resulting in beliefs it already holds, lower prior probability to parses resulting in beliefs it does not hold but which are consistent with its more abstract knowledge about semantic types of arguments to different relations, and still lower prior probability to parses that contradict its beliefs about which entity types can participate in which relations.
We present here a step in this direction: a probabilistic semantic parser that uses a large knowledge base (NELL) to form a prior probability distribution on the meanings of sentences it parses, and that "understands" each sentence either by identifying its existing beliefs that correspond to the sentence's meaning, or by creating new beliefs.
Answer: The answers are shown as follows:
* NELL