Context: During his time at his lab, Tesla observed unusual signals from his receiver which he concluded may be communications from another planet. He mentioned them in a letter to reporter Julian Hawthorne at the Philadelphia North American on 8 December 1899 and in a December 1900 letter about possible discoveries in the new century to the Red Cross Society where he referred to messages "from another world" that read "1... 2... 3...". Reporters treated it as a sensational story and jumped to the conclusion Tesla was hearing signals from Mars. He expanded on the signals he heard in a 9 February 1901 Collier's Weekly article "Talking With Planets" where he said it had not been immediately apparent to him that he was hearing "intelligently controlled signals" and that the signals could come from Mars, Venus, or other planets. It has been hypothesized that he may have intercepted Marconi's European experiments in July 1899—Marconi may have transmitted the letter S (dot/dot/dot) in a naval demonstration, the same three impulses that Tesla hinted at hearing in Colorado—or signals from another experimenter in wireless transmission.

Question: To what did Tesla attribute the unknown signals his radio received?

Question: What planet did the reporters decide the signals were from?

Question: What magazine did Tesla talk to about the strange signals?

Question: What is another possible explanation for the source of the signals?

Question: When did Marconi transmit similar signals to those Tesla received?


Context: In 1899, John Jacob Astor IV invested $100,000 for Tesla to further develop and produce a new lighting system. Instead, Tesla used the money to fund his Colorado Springs experiments.

Question: How much money did John Jacob Astor IV provide Tesla with?

Question: What did Astor expect the money be used for?

Question: What did Tesla spend Astor's money on?

Question: When did Astor provide the money?


Context: On 7 January 1900, Tesla left Colorado Springs.[citation needed] His lab was torn down in 1904, and its contents were sold two years later to satisfy a debt.

Question: When did Tesla depart from Colorado Springs?

Question: What happened to his lab?

Question: When was his lab destroyed?

Question: What happened to the things inside the lab after it was torn down?


Context: The Colorado experiments had prepared Tesla for the establishment of the trans-Atlantic wireless telecommunications facility known as Wardenclyffe near Shoreham, Long Island.

Question: What did Tesla establish following his Colorado experiments?

Question: What kind of facility was Wardenclyffe?

Question: Where was Wardenclyffe located?


Context: Tesla later approached Morgan to ask for more funds to build a more powerful transmitter. When asked where all the money had gone, Tesla responded by saying that he was affected by the Panic of 1901, which he (Morgan) had caused. Morgan was shocked by the reminder of his part in the stock market crash and by Tesla's breach of contract by asking for more funds. Tesla wrote another plea to Morgan, but it was also fruitless. Morgan still owed Tesla money on the original agreement, and Tesla had been facing foreclosure even before construction of the tower began.

Question: Who did Tesla try to get money from?

Question: On what did Tesla blame for the loss of the initial money?

Question: How did Morgan react to the request?


Context: In December 1901, Marconi successfully transmitted the letter S from England to Newfoundland, terminating Tesla's relationship with Morgan.[improper synthesis?] Over the next five years, Tesla wrote over 50 letters to Morgan, pleading for and demanding additional funding to complete the construction of Wardenclyffe. Tesla continued the project for another nine months. The tower was erected to its full 187 feet (57 m). In July 1903, Tesla wrote to Morgan that in addition to wireless communication, Wardenclyffe would be capable of wireless transmission of electric power. On 14 October 1904, Morgan finally replied through his secretary, stating, "It will be impossible for [me] to do anything in the matter," after Tesla had written to Morgan when the financier was meeting with the Archbishop of Canterbury in an attempt to appeal to his Christian spirit.

Question: How much correspondence did Tesla send Morgan in the five years following 1901?

Question: Why did Tesla want funds from Morgan?

Question: What happened in 1901?

Question: How tall was the tower's ultimate height?


Context: On his 50th birthday in 1906, Tesla demonstrated his 200 horsepower (150 kilowatts) 16,000 rpm bladeless turbine. During 1910–1911 at the Waterside Power Station in New York, several of his bladeless turbine engines were tested at 100–5,000 hp.

Question: How many horsepower did Tesla's bladeless turbine have?

Question: At what speed did the turbine operate?

Question: When did Tesla first show off the turbine?

Question: What was the horsepower put out by the turbines in Waterside Power Station


Context: Tesla invented a steam-powered mechanical oscillator—Tesla's oscillator. While experimenting with mechanical oscillators at his Houston Street lab, Tesla allegedly generated a resonance of several buildings. As the speed grew, it is said that the machine oscillated at the resonance frequency of his own building and, belatedly realizing the danger, he was forced to use a sledge hammer to terminate the experiment, just as the police arrived.:162–164 In February 1912, an article—"Nikola Tesla, Dreamer" by Allan L. Benson—was published in World Today, in which an artist's illustration appears showing the entire earth cracking in half with the caption, "Tesla claims that in a few weeks he could set the earth's crust into such a state of vibration that it would rise and fall hundreds of feet and practically destroy civilization. A continuation of this process would, he says, eventually split the earth in two."

Question: How was Tesla's mechanical oscillator powered?

Question: Where did he work on the oscillators?

Question: What unforeseen problem caused Tesla to hit his experiment with a sledgehammer?

Question: What publication published an article about Tesla in 1912?

Question: What did Tesla claim his resonant frequencies could do in the World Today article?


Context: Tesla theorized that the application of electricity to the brain enhanced intelligence. In 1912, he crafted "a plan to make dull students bright by saturating them unconsciously with electricity," wiring the walls of a schoolroom and, "saturating [the schoolroom] with infinitesimal electric waves vibrating at high frequency. The whole room will thus, Mr. Tesla claims, be converted into a health-giving and stimulating electromagnetic field or 'bath.'" The plan was, at least provisionally approved by then superintendent of New York City schools, William H. Maxwell.

Question: What did Tesla think could improve the brain's intelligence?

Question: What was Tesla's plan to make students "bright"?

Question: Who approved of this plan?

Question: What was Maxwell's job?


Context: Before World War I, Tesla sought overseas investors. After the war started, Tesla lost the funding he was receiving from his patents in European countries. Eventually, he sold Wardenclyffe for $20,000 ($472,500 in today's dollars). In 1917, around the time that the Wardenclyffe Tower was demolished by Boldt to make the land a more viable real estate asset, Tesla received AIEE's highest honor, the Edison Medal.

Question: Where did Tesla look for investors prior to WWI?

Question: What happened to Tesla's funding once the war begun?

Question: What did he do with Wardenclyffe?

Question: How much did Tesla receive from the sale of Wardenclyffe?

Question: What award was given to Tesla?


Context: In the August 1917 edition of the magazine Electrical Experimenter Tesla postulated that electricity could be used to locate submarines via using the reflection of an "electric ray" of "tremendous frequency," with the signal being viewed on a fluorescent screen (a system that has been noted to have a superficial resemblance to modern radar). Tesla was incorrect in his assumption that high frequency radio waves would penetrate water but Émile Girardeau, who helped develop France's first radar system in the 1930s, noted in 1953 that Tesla's general speculation that a very strong high frequency signal would be needed was correct stating "(Tesla) was prophesying or dreaming, since he had at his disposal no means of carrying them out, but one must add that if he was dreaming, at least he was dreaming correctly.":266

Question: In what magazine did Tesla talk about reflecting electricity off of submarine's hulls?

Question: How would the operator of observe the reflected signal?

Question: What later invention was Tesla's idea similar to?

Question: Who played a part in the development of radar in France?


Context: On 6 November 1915, a Reuters news agency report from London had the 1915 Nobel Prize in Physics awarded to Thomas Edison and Nikola Tesla; however, on 15 November, a Reuters story from Stockholm stated the prize that year was being awarded to Sir William Henry Bragg and William Lawrence Bragg "for their services in the analysis of crystal structure by means of X-rays.":245 There were unsubstantiated rumors at the time that Tesla and/or Edison had refused the prize.:245 The Nobel Foundation said, "Any rumor that a person has not been given a Nobel Prize because he has made known his intention to refuse the reward is ridiculous"; a recipient could only decline a Nobel Prize after he is announced a winner.:245

Question: Who did Reuters claim won the 1915 Nobel prize in Physics?

Question: Who actually won the prize?

Question: What was the rumored reason Edison and Tesla were not awarded the prize?

Question: According to the Nobel Foundation what has to happen before someone can decline a prize?


Context: There have been subsequent claims by Tesla biographers that Edison and Tesla were the original recipients and that neither was given the award because of their animosity toward each other; that each sought to minimize the other's achievements and right to win the award; that both refused ever to accept the award if the other received it first; that both rejected any possibility of sharing it; and even that a wealthy Edison refused it to keep Tesla from getting the $20,000 prize money.:245

Question: What was another possible reason that Tesla and Edison did not get the award?.


Context: In the years after these rumors, neither Tesla nor Edison won the prize (although Edison did receive one of 38 possible bids in 1915 and Tesla did receive one of 38 possible bids in 1937).

Question: How many possible bids for the prize were there in 1915?

Question: Who received a bid in 1915?

Question: In what year did Tesla receive a Nobel Prize bid?


Context: In 1928, Tesla received his last patent, U.S. Patent 1,655,114, for a biplane capable of taking off vertically (VTOL aircraft) and then be "gradually tilted through manipulation of the elevator devices" in flight until it was flying like a conventional plane. Tesla thought the plane would sell for less than $1,000.:251 Although the aircraft was probably impractical, it may be the earliest known design for what became the tiltrotor/tilt-wing concept as well as the earliest proposal for the use of turbine engines in rotor aircraft.[improper synthesis?]

Question: What was the patent number for Tesla's final patent?

Question: For what invention was U.S. Patent 1,655,114 granted?

Question: What was Tesla's belief as to the selling price of the biplane?

Question: What kind of engines did the biplane design have?


Context: Starting in 1934, the Westinghouse Electric & Manufacturing Company began paying Tesla $125 per month as well as paying his rent at the Hotel New Yorker, expenses the Company would pay for the rest of Tesla's life. Accounts on how this came about vary. Several sources say Westinghouse was worried about potential bad publicity surrounding the impoverished conditions their former star inventor was living under. It has been described as being couched in the form of a "consulting fee" to get around Tesla's aversion to accept charity, or by one biographer (Marc Seifer), as a type of unspecified settlement.

Question: What was Tesla's salary from Westinghouse in 1934?

Question: What other compensation did Tesla get from Westinghouse?

Question: For how long did Tesla receive this compensation?

Question: What was Westinghouse potentially trying to avoid by paying Tesla?


Context: In 1935, in an annual birthday celebration interview, Tesla announced a method of transmitting mechanical energy with minimal loss over any terrestrial distance, a related new means of communication, and a method of accurately determining the location of underground mineral deposits.

Question: What did Tesla claim to be able to transmit?

Question: How far did he claim the mechanical energy could be transmitted?

Question: How much energy loss did he expect would occur?

Question: What did he hope to locate underground?

Question: When did Tesla make these claims?


Context: In the fall of 1937, after midnight one night, Tesla left the Hotel New Yorker to make his regular commute to the cathedral and the library to feed the pigeons. While crossing a street a couple of blocks from the hotel, Tesla was unable to dodge a moving taxicab and was thrown heavily to the ground. Tesla's back was severely wrenched and three of his ribs were broken in the accident (the full extent of his injuries will never be known; Tesla refused to consult a doctor—an almost lifelong custom). Tesla didn't raise any question as to who was at fault and refused medical aid, only asking to be taken to his hotel via cab. Tesla was bedridden for some months and was unable to continue feeding pigeons from his window; soon, they failed to come. In early 1938, Tesla was able to get up. He at once resumed the pigeon-feeding walks on a much more limited scale, but frequently had a messenger act for him.

Question: What was Tesla on his way to do when he was struck by the cab?

Question: Who did Tesla refuse to see following the accident?

Question: What happened to his ribs in the accident?

Question: When did he first get out of bed following the accident?

Question: When did the accident occur?


Context: Later in life, Tesla made claims concerning a "teleforce" weapon after studying the Van de Graaff generator. The press variably referred to it as a "peace ray" or death ray. Tesla described the weapon as capable of being used against ground-based infantry or for anti-aircraft purposes.

Question: What kind of weapon did Tesla talk about?

Question: What was he studying that gave him the teleforce weapon idea?

Question: What ground based group did Tesla think the weapon could be used on?

Question: What was another use for the weapon?

Question: Other than a peace-ray, what did reporters call the weapon?


Context: In 1937, at a luncheon in his honor concerning the death ray, Tesla stated, "But it is not an experiment ... I have built, demonstrated and used it. Only a little time will pass before I can give it to the world." His records indicate that the device is based on a narrow stream of small tungsten pellets that are accelerated via high voltage (by means akin to his magnifying transformer).

Question: When did Tesla claim to have built the weapon?

Question: What was the occasion when he claimed he'd made the death ray?

Question: Of what material were the death rays pellets made?

Question: What was used to accelerate the tungsten pellets?

Question: How much time did he claim had to pass before he gave the ray to the world?


Context: During the same year, Tesla wrote a treatise, The Art of Projecting Concentrated Non-dispersive Energy through the Natural Media, concerning charged particle beam weapons. Tesla published the document in an attempt to expound on the technical description of a "superweapon that would put an end to all war." This treatise is currently in the Nikola Tesla Museum archive in Belgrade. It describes an open-ended vacuum tube with a gas jet seal that allows particles to exit, a method of charging particles to millions of volts, and a method of creating and directing non-dispersive particle streams (through electrostatic repulsion). Tesla tried to interest the US War Department, the United Kingdom, the Soviet Union, and Yugoslavia in the device.

Question: What kind of weapons did Tesla's treatise concern?

Question: Where can the Treatise be found?

Question: In what city is the Nikola Tesla Museum?

Question: How many volts could particles be charged to in his weapon design?

Question: What did he claim the weapon would end?


Context: During the period in which the negotiations were being conducted, Tesla said that efforts had been made to steal the invention. His room had been entered and his papers had been scrutinized, but the thieves, or spies, left empty-handed. He said that there was no danger that his invention could be stolen, for he had at no time committed any part of it to paper; the blueprint for the teleforce weapon was all in his mind.

Question: What was Tesla afraid someone was trying to do with his invention?

Question: Where did he claim the blueprint was stored?

Question: According to Tesla what had been gone over by the thieves, or spies who entered his room?


Context: On 7 January 1943, at the age of 86, Tesla died alone in room 3327 of the New Yorker Hotel. His body was later found by maid Alice Monaghan after she had entered Tesla's room, ignoring the "do not disturb" sign that Tesla had placed on his door two days earlier. Assistant medical examiner H.W. Wembly examined the body and ruled that the cause of death had been coronary thrombosis. Tesla's remains were taken to the Frank E. Campbell Funeral Home at Madison Ave. and 81st St. A long-time friend and supporter of Tesla, Hugo Gernsback, commissioned a sculptor to create a death mask, now displayed in the Nikola Tesla Museum.

Question: How old was Tesla when he died?

Question: What was the date of Tesla's death?

Question: Who first discovered his body?

Question: What had been left hanging on the door to Tesla's room?

Question: What was given as the cause of death?


Context: Two days later, the FBI ordered the Alien Property Custodian to seize Tesla's belongings, even though Tesla was an American citizen. Tesla's entire estate from the Hotel New Yorker and other New York City hotels was transported to the Manhattan Storage and Warehouse Company under the Office of Alien Property (OAP) seal. John G. Trump, a professor at M.I.T. and a well-known electrical engineer serving as a technical aide to the National Defense Research Committee, was called in to analyze the Tesla items in OAP custody. After a three-day investigation, Trump's report concluded that there was nothing which would constitute a hazard in unfriendly hands, stating:

Question: What happened to Tesla's belongings?

Question: Who analyzed Tesla's belongings?

Question: What did he find that could constitute a hazard?

Question: To where were the belongings taken?


Context: On 10 January 1943, New York City mayor Fiorello La Guardia read a eulogy written by Slovene-American author Louis Adamic live over the WNYC radio while violin pieces "Ave Maria" and "Tamo daleko" were played in the background. On 12 January, two thousand people attended a state funeral for Tesla at the Cathedral of Saint John the Divine. After the funeral, Tesla's body was taken to the Ferncliff Cemetery in Ardsley, New York, where it was later cremated. The following day, a second service was conducted by prominent priests in the Trinity Chapel (today's Serbian Orthodox Cathedral of Saint Sava) in New York City.

Question:
Who gave Tesla's eulogy?
Who wrote the eulogy?
What day was Tesla's funeral?
How many people attended the funeral?
Where was the funeral held?


Context: In 1952, following pressure from Tesla's nephew, Sava Kosanović, Tesla's entire estate was shipped to Belgrade in 80 trunks marked N.T. In 1957, Kosanović's secretary Charlotte Muzar transported Tesla's ashes from the United States to Belgrade. The ashes are displayed in a gold-plated sphere on a marble pedestal in the Nikola Tesla Museum.

Question: Where was Tesla's property sent?

Question: Who was Tesla's nephew?

Question: Who transported Tesla's ashes from the US.

Question: Where were the ashes sent?

Question: When are the ashes now?


Context: Tesla obtained around 300 patents worldwide for his inventions. Some of Tesla's patents are not accounted for, and various sources have discovered some that have lain hidden in patent archives. There are a minimum of 278 patents issued to Tesla in 26 countries that have been accounted for. Many of Tesla's patents were in the United States, Britain, and Canada, but many other patents were approved in countries around the globe.:62 Many inventions developed by Tesla were not put into patent protection.

Question: What was the total number of patents that Tesla had?

Question: In how many countries did Tesla hold patents?

Question: Other than the US and Britain what was the other main country that Tesla had patents granted?

Question: Where were missing patents later found?


Context: Tesla worked every day from 9:00 a.m. until 6:00 p.m. or later, with dinner from exactly 8:10 p.m., at Delmonico's restaurant and later the Waldorf-Astoria Hotel. Tesla would telephone his dinner order to the headwaiter, who also could be the only one to serve him. "The meal was required to be ready at eight o'clock ... He dined alone, except on the rare occasions when he would give a dinner to a group to meet his social obligations. Tesla would then resume his work, often until 3:00 a.m.":283, 286

Question: At what time did Tesla get dinner?

Question: Before dinner what were Tesla's working hours?

Question: On some nights how late did Tesla work until?

Question: Who served his dinner?


Context: For exercise, Tesla walked between 8 to 10 miles per day. He squished his toes one hundred times for each foot every night, saying that it stimulated his brain cells.

Question: What was the daily distance walked by Tesla?

Question: Why did he walk?

Question: What did he do to his feet at night?

Question: What was he hoping to stimulate with the toe squishing?


Context: In an interview with newspaper editor Arthur Brisbane, Tesla said that he did not believe in telepathy, stating, "Suppose I made up my mind to murder you," he said, "In a second you would know it. Now, isn't that wonderful? By what process does the mind get at all this?" In the same interview, Tesla said that he believed that all fundamental laws could be reduced to one.

Question: What did Tesla tell Brisbane he didn't believe in?

Question: What was Brisbane's job?

Question: Down to how many laws did Tesla believe the fundamental laws could be reduced?


Context: Near the end of his life, Tesla walked to the park every day to feed the pigeons and even brought injured ones into his hotel room to nurse back to health. He said that he had been visited by a specific injured white pigeon daily. Tesla spent over $2,000, including building a device that comfortably supported her so her bones could heal, to fix her broken wing and leg. Tesla stated,

Question: What animals did Tesla care for?

Question: How much did Tesla spend on the injured pigeon?

Question: What was wrong with the pigeon?

Question: Where did Tesla go to feed the pigeons daily?

Question: To what place did he bring the injured pigeons to take care of them?


Context: Tesla was 6 feet 2 inches (1.88 m) tall and weighed 142 pounds (64 kg), with almost no weight variance from 1888 to about 1926.:292 He was an elegant, stylish figure in New York City, meticulous in his grooming, clothing, and regimented in his daily activities.

Question: How much did Tesla weigh?

Question: How tall was Tesla?

Question: In what time frame did Tesla weigh almost exactly the same amount?

Question: Where did he live while he was looking good?


Context: Tesla read many works, memorizing complete books, and supposedly possessed a photographic memory.:33 He was a polyglot, speaking eight languages: Serbo-Croatian, Czech, English, French, German, Hungarian, Italian, and Latin.:282 Tesla related in his autobiography that he experienced detailed moments of inspiration. During his early life, Tesla was repeatedly stricken with illness. He suffered a peculiar affliction in which blinding flashes of light would appear before his eyes, often accompanied by visions.:33 Often, the visions were linked to a word or idea he might have come across; at other times they would provide the solution to a particular problem he had encountered. Just by hearing the name of an item, he would be able to envision it in realistic detail.:33 Tesla would visualize an invention in his mind with extreme precision, including all dimensions, before moving to the construction stage, a technique sometimes known as picture thinking. He typically did not make drawings by hand but worked from memory. Beginning in his childhood, Tesla had frequent flashbacks to events that had happened previously in his life.:33

Question: How many languages did Tesla know?

Question: What did Tesla see during his strange illnesses when he was young?

Question: What is another term for Tesla's visualization ability?

Question: What other visual phenomena accompanied Tesla's visions?

Question: What kind of memory was Tesla thought to have?


Context: During his second year of study at Graz, Tesla developed a passion for (and became very proficient at) billiards, chess and card-playing, sometimes spending more than 48 hours in a stretch at a gaming table.:43, 301 On one occasion at his laboratory, Tesla worked for a period of 84 hours without sleep or rest.:208 Kenneth Swezey, a journalist whom Tesla had befriended, confirmed that Tesla rarely slept. Swezey recalled one morning when Tesla called him at 3 a.m.: "I was sleeping in my room like one dead ... Suddenly, the telephone ring awakened me ... [Tesla] spoke animatedly, with pauses, [as he] ... work[ed] out a problem, comparing one theory to another, commenting; and when he felt he had arrived at the solution, he suddenly closed the telephone."

Question: How long would Tesla spend gambling sometimes?

Question: What amount of time was the longest that Tesla spent working without stopping to rest?

Question: Where was Tesla studying when he started playing cards and billiards?

Question: Who did Tesla call in the middle of the night?

Question: What was Kenneth Swezey's job?


Context: Tesla never married; he said his chastity was very helpful to his scientific abilities.:33 However, toward the end of his life, he told a reporter, "Sometimes I feel that by not marrying, I made too great a sacrifice to my work ..." There have been numerous accounts of women vying for Tesla's affection, even some madly in love with him.[citation needed] Tesla, though polite and soft-spoken, did not have any known relationships.

Question: What characteristic did Tesla say helped his scientific abilities?

Question: Who reportedly wanted Tesla's company?

Question: When did Tesla admit to a reporter that maybe he'd sacrificed too much by not having a relationship?


Context: Tesla was asocial and prone to seclude himself with his work. However, when he did engage in a social life, many people spoke very positively and admiringly of Tesla. Robert Underwood Johnson described him as attaining a "distinguished sweetness, sincerity, modesty, refinement, generosity, and force." His loyal secretary, Dorothy Skerrit, wrote: "his genial smile and nobility of bearing always denoted the gentlemanly characteristics that were so ingrained in his soul." Tesla's friend, Julian Hawthorne, wrote, "seldom did one meet a scientist or engineer who was also a poet, a philosopher, an appreciator of fine music, a linguist, and a connoisseur of food and drink.":80

Question: Who was Tesla's secretary?

Question: Who said Tesla had a "distinguished sweetness"?

Question: What was Tesla likely to do with his work?

Question: With what word was Tesla's sociability described?

Question: What was Julian Hawthorne's relation to Tesla?


Context: Tesla was a good friend of Francis Marion Crawford, Robert Underwood Johnson, Stanford White, Fritz Lowenstein, George Scherff, and Kenneth Swezey. In middle age, Tesla became a close friend of Mark Twain; they spent a lot of time together in his lab and elsewhere. Twain notably described Tesla's induction motor invention as "the most valuable patent since the telephone." In the late 1920s, Tesla also befriended George Sylvester Viereck, a poet, writer, mystic, and later, a Nazi propagandist. Tesla occasionally attended dinner parties held by Viereck and his wife.

Question: What famous writer was Tesla's good friend?

Question: Where did Tesla and Twain hang out?

Question: When did Tesla become friends with Viereck?


Context: Tesla could be harsh at times and openly expressed disgust for overweight people, such as when he fired a secretary because of her weight.:110 He was quick to criticize clothing; on several occasions, Tesla directed a subordinate to go home and change her dress.:33

Question: Who was Tesla prejudiced against?

Question: Who did he fire?

Question: Why did he fire the secretary?

Question: What did he make the employee do when he didn't like her outfit?


Context: Tesla exhibited a pre-atomic understanding of physics in his writings; he disagreed with the theory of atoms being composed of smaller subatomic particles, stating there was no such thing as an electron creating an electric charge (he believed that if electrons existed at all, they were some fourth state of matter or "sub-atom" that could only exist in an experimental vacuum and that they had nothing to do with electricity).:249 Tesla believed that atoms are immutable—they could not change state or be split in any way. He was a believer in the 19th century concept of an all pervasive "ether" that transmitted electrical energy.

Question: What subatomic particle did Tesla deny the existence of?

Question: What did he think was everywhere in the universe?

Question: What did he believe the ether did?

Question: To what century did the idea of ether belong?


Context: Tesla was generally antagonistic towards theories about the conversion of matter into energy.:247 He was also critical of Einstein's theory of relativity, saying:

Question: Whose theory did Tesla disagree with?

Question: What was Tesla's attitude toward the idea that matter could be turned into energy?

Question: Which theory of Einstein's did Tesla speak critically toward?


Context: Tesla claimed to have developed his own physical principle regarding matter and energy that he started working on in 1892, and in 1937, at age 81, claimed in a letter to have completed a "dynamic theory of gravity" that "[would] put an end to idle speculations and false conceptions, as that of curved space." He stated that the theory was "worked out in all details" and that he hoped to soon give it to the world. Further elucidation of his theory was never found in his writings.:309

Question: What "dynamic theory" did Tesla say he finished in 1937?

Question: When did Tesla start working on the problem of energy and matter?

Question: What shape of space did Tesla consider a "false conception"?

Question: How old was Tesla when he wrote that he'd completed his dynamic theory of gravity?


Context: Tesla, like many of his era, became a proponent of an imposed selective breeding version of eugenics. His opinion stemmed from the belief that humans' "pity" had interfered with the natural "ruthless workings of nature," rather than from conceptions of a "master race" or inherent superiority of one person over another. His advocacy of it was, however, to push it further. In a 1937 interview, he stated:

Question: What idea was Tesla a fan of?

Question: What was his belief as to what nature was supposed to be?

Question: What attribute of humans interfered with nature's ruthlessness in Tesla's opinion?

Question: When did he talk about his beliefs in an interview?


Context: In 1926, Tesla commented on the ills of the social subservience of women and the struggle of women toward gender equality, and indicated that humanity's future would be run by "Queen Bees." He believed that women would become the dominant sex in the future.

Question: Who did Tesla think would run the world of the future?

Question: When did he talk about his thoughts on gender?

Question: What term did he use for what he believed would be humanity's future rulers?


Context: Tesla made predictions about the relevant issues of a post-World War I environment in a printed article, "Science and Discovery are the great Forces which will lead to the Consummation of the War" (20 December 1914). Tesla believed that the League of Nations was not a remedy for the times and issues.[citation needed]

Question: The issues of what era did Tesla's article talk about?

Question: What were the "great Forces" mentioned in the article's title?

Question: When was the article published?

Question: What entity did Tesla believe would not be able to solve the problems of the time?


Context: Tesla was raised an Orthodox Christian. Later in his life, he did not consider himself to be a "believer in the orthodox sense," and opposed religious fanaticism. Despite this, he had a profound respect for both Buddhism and Christianity.

Question: What religion did Tesla grow up in?

Question: What type of religious behavior was Tesla against?

Question: Which two religions did Tesla express respect for?


Context: However, his religious views remain uncertain due to other statements that he made. For example, in his article, "A Machine to End War", published in 1937, Tesla stated:

Question: What article was published in 1937?

Question: Because of certain statements what was the believed state of his religious views?

Question: In the article's title, what did the machine hope to end?


Context: Tesla wrote a number of books and articles for magazines and journals. Among his books are My Inventions: The Autobiography of Nikola Tesla, compiled and edited by Ben Johnston; The Fantastic Inventions of Nikola Tesla, compiled and edited by David Hatcher Childress; and The Tesla Papers.

Question:
What did Tesla write?
Who published Tesla's writings?
Who edited Tesla's autobiography?


Context: Many of Tesla's writings are freely available on the web, including the article "The Problem of Increasing Human Energy," published in The Century Magazine in 1900, and the article "Experiments With Alternate Currents Of High Potential And High Frequency," published in his book Inventions, Researches and Writings of Nikola Tesla.

Question: Where can a lot Tesla's writings be found?

Question: When was his article published in Century Magazine?

Question: What was one of Tesla's books where articles can be read?


Context: Tesla's legacy has endured in books, films, radio, TV, music, live theater, comics and video games. The impact of the technologies invented or envisioned by Tesla is a recurring theme in several types of science fiction.

Question: What kind of fiction is Tesla's work featured in?

Question: What kind of media can references to Tesla be found in

Question: How many types of science fiction have been impacted by Tesla?


Context: On Tesla's 75th birthday in 1931, Time magazine put him on its cover. The cover caption "All the world's his power house" noted his contribution to electrical power generation. He received congratulatory letters from more than 70 pioneers in science and engineering, including Albert Einstein.

Question: On which magazine's cover did Tesla appear in 1931

Question: For what occasion was he put on the cover?

Question: To which technology type that Tesla worked on did the caption refer to?

Question: What famous person congratulated him?

Question: How many other important people sent letters?


Context: Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm.

Question: What branch of theoretical computer science deals with broadly classifying computational problems by difficulty and class of relationship?

Question: By what main attribute are computational problems classified utilizing computational complexity theory?

Question: What is the term for a task that generally lends itself to being solved by a computer?


Context: A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do.

Question: What measure of a computational problem broadly defines the inherent difficulty of the solution?

Question: What method is used to intuitively assess or quantify the amount of resources required to solve a computational problem?

Question: What are two basic primary resources used to guage complexity?

Question: What unit is measured to determine circuit complexity?

Question: What practical role does defining the complexity of problems play in everyday computing?


Context: Closely related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. More precisely, it tries to classify problems that can or cannot be solved with appropriately restricted resources. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kind of problems can, in principle, be solved algorithmically.

Question: What two fields of theoretical computer science closely mirror computational complexity theory?

Question: What field of computer science analyzes the resource requirements of a specific algorithm isolated unto itself within a given problem?

Question: What field of computer science analyzes all possible algorithms in aggregate to determine the resource requirements needed to solve to a given problem?

Question: What field of computer science is primarily concerned with determining the likelihood of whether or not a problem can ultimately be solved using algorithms?


Context: A computational problem can be viewed as an infinite collection of instances together with a solution for every instance. The input string for a computational problem is referred to as a problem instance, and should not be confused with the problem itself. In computational complexity theory, a problem refers to the abstract question to be solved. In contrast, an instance of this problem is a rather concrete utterance, which can serve as the input for a decision problem. For example, consider the problem of primality testing. The instance is a number (e.g. 15) and the solution is "yes" if the number is prime and "no" otherwise (in this case "no"). Stated another way, the instance is a particular input to the problem, and the solution is the output corresponding to the given input.

Question: What is the name given to the input string of a computational problem?

Question: In computational complexity theory, what is the term given to describe the baseline abstract question needing to be solved?

Question: Is a problem instance typically characterized as abstract or concrete?

Question: What is another name for any given measure of input associated with a problem?

Question: What is the general term used to describe the output to any given input in a problem instance?


Context: To further highlight the difference between a problem and an instance, consider the following instance of the decision version of the traveling salesman problem: Is there a route of at most 2000 kilometres passing through all of Germany's 15 largest cities? The quantitative answer to this particular problem instance is of little use for solving other instances of the problem, such as asking for a round trip through all sites in Milan whose total length is at most 10 km. For this reason, complexity theory addresses computational problems and not particular problem instances.

Question: By how many kilometers does the traveling salesman problem seek to classify a route between the 15 largest cities in Germany?

Question: What is one example of an instance that the quantitative answer to the traveling salesman problem fails to answer?

Question: What does computational complexity theory most specifically seek to answer?


Context: When considering computational problems, a problem instance is a string over an alphabet. Usually, the alphabet is taken to be the binary alphabet (i.e., the set {0,1}), and thus the strings are bitstrings. As in a real-world computer, mathematical objects other than bitstrings must be suitably encoded. For example, integers can be represented in binary notation, and graphs can be encoded directly via their adjacency matrices, or by encoding their adjacency lists in binary.

Question: In a computational problem, what can be described as a string over an alphabet?

Question: What is the name of the alphabet is most commonly used in a problem instance?

Question: What is another term for the string of a problem instance?

Question: In the encoding of mathematical objects, what is the way in which integers are commonly expressed?

Question: What is one way in which graphs can be encoded?


Context: Decision problems are one of the central objects of study in computational complexity theory. A decision problem is a special type of computational problem whose answer is either yes or no, or alternately either 1 or 0. A decision problem can be viewed as a formal language, where the members of the language are instances whose output is yes, and the non-members are those instances whose output is no. The objective is to decide, with the aid of an algorithm, whether a given input string is a member of the formal language under consideration. If the algorithm deciding this problem returns the answer yes, the algorithm is said to accept the input string, otherwise it is said to reject the input.

Question: What kind of problems are one of the main topics studied in computational complexity theory?

Question: What are the two simple word responses to a decision problem?

Question: What are the two integer responses to a decision problem?

Question: What will the output be for a member of the language of a decision problem?

Question: What answer denotes that an algorithm has accepted an input string?


Context: An example of a decision problem is the following. The input is an arbitrary graph. The problem consists in deciding whether the given graph is connected, or not. The formal language associated with this decision problem is then the set of all connected graphs—of course, to obtain a precise definition of this language, one has to decide how graphs are encoded as binary strings.

Question: What kind of graph is an example of an input used in a decision problem?

Question: What is the term for the set of all connected graphs related to this decision problem?

Question: What encoding decision needs to be made in order to determine an exact definition of the formal language?


Context: A function problem is a computational problem where a single output (of a total function) is expected for every input, but the output is more complex than that of a decision problem, that is, it isn't just yes or no. Notable examples include the traveling salesman problem and the integer factorization problem.

Question: A function problem is an example of what?

Question: How many outputs are expected for each input in a function problem?

Question: The traveling salesman problem is an example of what type of problem?

Question: In addition to the traveling salesman problem, what is another example of a function problem?

Question: Is the output of a functional problem typically characterized by a simple or complex answer?


Context: It is tempting to think that the notion of function problems is much richer than the notion of decision problems. However, this is not really the case, since function problems can be recast as decision problems. For example, the multiplication of two integers can be expressed as the set of triples (a, b, c) such that the relation a × b = c holds. Deciding whether a given triple is a member of this set corresponds to solving the problem of multiplying two numbers.

Question: How can function problems typically be restated?

Question: If two integers are multiplied and output a value, what is this expression set called?


Context: To measure the difficulty of solving a computational problem, one may wish to see how much time the best algorithm requires to solve the problem. However, the running time may, in general, depend on the instance. In particular, larger instances will require more time to solve. Thus the time required to solve a problem (or the space required, or any measure of complexity) is calculated as a function of the size of the instance. This is usually taken to be the size of the input in bits. Complexity theory is interested in how algorithms scale with an increase in the input size. For instance, in the problem of finding whether a graph is connected, how much more time does it take to solve a problem for a graph with 2n vertices compared to the time taken for a graph with n vertices?

Question: What is a commonly used measurement used to determine the complexity of a computational problem?

Question: What is one variable on which the running time may be contingent?

Question: How is the time needed to obtain the solution to a problem calculated?

Question: In what unit is the size of the input measured?

Question: Complexity theory seeks to define the relationship between the scale of algorithms with respect to what other variable?


Context: If the input size is n, the time taken can be expressed as a function of n. Since the time taken on different inputs of the same size can be different, the worst-case time complexity T(n) is defined to be the maximum time taken over all inputs of size n. If T(n) is a polynomial in n, then the algorithm is said to be a polynomial time algorithm. Cobham's thesis says that a problem can be solved with a feasible amount of resources if it admits a polynomial time algorithm.

Question: Whose thesis states that the solution to a problem is solvable with reasonable resources assuming it allows for a polynomial time algorithm?

Question: If input size is is equal to n, what can respectively be assumed is the function of n?

Question: What term corresponds to the maximum measurement of time across all functions of n?

Question: How is worst-case time complexity written as an expression?

Question: Assuming that T represents a polynomial in T(n), what is the term given to the corresponding algorithm?


Context: A Turing machine is a mathematical model of a general computing machine. It is a theoretical device that manipulates symbols contained on a strip of tape. Turing machines are not intended as a practical computing technology, but rather as a thought experiment representing a computing machine—anything from an advanced supercomputer to a mathematician with a pencil and paper. It is believed that if a problem can be solved by an algorithm, there exists a Turing machine that solves the problem. Indeed, this is the statement of the Church–Turing thesis. Furthermore, it is known that everything that can be computed on other models of computation known to us today, such as a RAM machine, Conway's Game of Life, cellular automata or any programming language can be computed on a Turing machine. Since Turing machines are easy to analyze mathematically, and are believed to be as powerful as any other model of computation, the Turing machine is the most commonly used model in complexity theory.

Question: What is the term for a mathematical model that theoretically represents a general computing machine?

Question: It is generally assumed that a Turing machine can solve anything capable of also being solved using what?

Question: What is the most commonplace model utilized in complexity theory?

Question: What does a Turing machine handle on a strip of tape?


Context: A deterministic Turing machine is the most basic Turing machine, which uses a fixed set of rules to determine its future actions. A probabilistic Turing machine is a deterministic Turing machine with an extra supply of random bits. The ability to make probabilistic decisions often helps algorithms solve problems more efficiently. Algorithms that use random bits are called randomized algorithms. A non-deterministic Turing machine is a deterministic Turing machine with an added feature of non-determinism, which allows a Turing machine to have multiple possible future actions from a given state. One way to view non-determinism is that the Turing machine branches into many possible computational paths at each step, and if it solves the problem in any of these branches, it is said to have solved the problem. Clearly, this model is not meant to be a physically realizable model, it is just a theoretically interesting abstract machine that gives rise to particularly interesting complexity classes. For examples, see non-deterministic algorithm.

Question: What is generally considered to be the most basic iteration of a Turing machine?

Question: What fixed set of factors determine the actions of a deterministic Turing machine

Question: What is the term used to identify a deterministic Turing machine that has additional random bits?

Question: What type of Turing machine is capable of multiple actions and extends into a variety of computational paths?

Question: What is the term given to algorithms that utilize random bits?


Context: Many types of Turing machines are used to define complexity classes, such as deterministic Turing machines, probabilistic Turing machines, non-deterministic Turing machines, quantum Turing machines, symmetric Turing machines and alternating Turing machines. They are all equally powerful in principle, but when resources (such as time or space) are bounded, some of these may be more powerful than others.

Question: Turing machines are commonly employed to define what?

Question: What are two factors that directly effect how powerful a Turing machine may or may not be?

Question: In the determination of complexity classes, what are two examples of types of Turing machines?


Context: Many machine models different from the standard multi-tape Turing machines have been proposed in the literature, for example random access machines. Perhaps surprisingly, each of these models can be converted to another without providing any extra computational power. The time and memory consumption of these alternate models may vary. What all these models have in common is that the machines operate deterministically.

Question: What is an example of a machine model that deviates from a generally accepted multi-tape Turing machine?

Question: In considering Turing machines and alternate variables, what measurement left unaffected by conversion between machine models?

Question: What two resources commonly consumed by alternate models are typically known to vary?

Question: What commonality do alternate machine models, such as random access machines, share with Turing machines?


Context: However, some computational problems are easier to analyze in terms of more unusual resources. For example, a non-deterministic Turing machine is a computational model that is allowed to branch out to check many different possibilities at once. The non-deterministic Turing machine has very little to do with how we physically want to compute algorithms, but its branching exactly captures many of the mathematical models we want to analyze, so that non-deterministic time is a very important resource in analyzing computational problems.

Question: What type of Turing machine can be characterized by checking multiple possibilities at the same time?

Question: What often affects or facilitates ease of analysis in computational problems?

Question: A non-deterministic Turing machine has the ability to capture what facet of useful analysis?

Question: What is the most critical resource in the analysis of computational problems associated with non-deterministic Turing machines?


Context: For a precise definition of what it means to solve a problem using a given amount of time and space, a computational model such as the deterministic Turing machine is used. The time required by a deterministic Turing machine M on input x is the total number of state transitions, or steps, the machine makes before it halts and outputs the answer ("yes" or "no"). A Turing machine M is said to operate within time f(n), if the time required by M on each input of length n is at most f(n). A decision problem A can be solved in time f(n) if there exists a Turing machine operating in time f(n) that solves the problem. Since complexity theory is interested in classifying problems based on their difficulty, one defines sets of problems based on some criteria. For instance, the set of problems solvable within time f(n) on a deterministic Turing machine is then denoted by DTIME(f(n)).

Question: The time required to output an answer on a deterministic Turing machine is expressed as what?

Question: Complexity theory classifies problems based on what primary attribute?

Question: What is the expression used to identify any given series of problems capable of being solved within time on a deterministic Turing machine?

Question: What is the most critical resource measured to in assessing the determination of a Turing machine's ability to solve any given set of problems?


Context: Analogous definitions can be made for space requirements. Although time and space are the most well-known complexity resources, any complexity measure can be viewed as a computational resource. Complexity measures are very generally defined by the Blum complexity axioms. Other complexity measures used in complexity theory include communication complexity, circuit complexity, and decision tree complexity.

Question: Time and space are both examples of what type of resource?

Question: A complexity resource can also be described as what other type of resource?

Question: What is typically used to broadly define complexity measures?

Question: Communication complexity is an example of what type of measure?

Question: Decision tree is an example of what type of measure?


Context: The best, worst and average case complexity refer to three different ways of measuring the time complexity (or any other complexity measure) of different inputs of the same size. Since some inputs of size n may be faster to solve than others, we define the following complexities:

Question: What are the three primary expressions used to represent case complexity?

Question: Case complexity likelihoods provide variable probabilities of what general measure?

Question: What is one common example of a critical complexity measure?

Question: Case complexities provide three likelihoods of what differing variable that remains the same size?


Context: For example, consider the deterministic sorting algorithm quicksort. This solves the problem of sorting a list of integers that is given as the input. The worst-case is when the input is sorted or sorted in reverse order, and the algorithm takes time O(n2) for this case. If we assume that all possible permutations of the input list are equally likely, the average time taken for sorting is O(n log n). The best case occurs when each pivoting divides the list in half, also needing O(n log n) time.

Question: What provides a solution to a list of integers provided as input that ned to be sorted?

Question: When extensive time is required to sort integers, this represents what case complexity?

Question: What is the expression used to denote a worst case complexity as expressed by time taken?


Context: To classify the computation time (or similar resources, such as space consumption), one is interested in proving upper and lower bounds on the minimum amount of time required by the most efficient algorithm solving a given problem. The complexity of an algorithm is usually taken to be its worst-case complexity, unless specified otherwise. Analyzing a particular algorithm falls under the field of analysis of algorithms. To show an upper bound T(n) on the time complexity of a problem, one needs to show only that there is a particular algorithm with running time at most T(n). However, proving lower bounds is much more difficult, since lower bounds make a statement about all possible algorithms that solve a given problem. The phrase "all possible algorithms" includes not just the algorithms known today, but any algorithm that might be discovered in the future. To show a lower bound of T(n) for a problem requires showing that no algorithm can have time complexity lower than T(n).

Question: Classification of resources is contingent on determining the upper and lower bounds of minimum time required by what?

Question: The analysis of a specific algorithm is typically assigned to what field of computational science?

Question: Which bound of time is more difficult to establish?

Question: A specific algorithm demonstrating T(n) represents what measure of time complexity?

Question: What is the colloquial phrase used to convey the continuum of algorithms with unlimited availability irrespective of time?


Context: Upper and lower bounds are usually stated using the big O notation, which hides constant factors and smaller terms. This makes the bounds independent of the specific details of the computational model used. For instance, if T(n) = 7n2 + 15n + 40, in big O notation one would write T(n) = O(n2).

Question: What expression is generally used to convey upper or lower bounds?

Question: What does a big O notation hide?

Question: How would one write T(n) = 7n2 + 15n + 40 in big O notation?

Question: Big O notation provides autonomy to upper and lower bounds with relationship to what?


Context: Of course, some complexity classes have complicated definitions that do not fit into this framework. Thus, a typical complexity class has a definition like the following:

Question: What has complicated definitions that prevent classification into a framework?

Question: Complexity classes are generally classified into what?

Question: Difficulty in establishing a framework for complexity classes can be caused by what variable?


Context: But bounding the computation time above by some concrete function f(n) often yields complexity classes that depend on the chosen machine model. For instance, the language {xx | x is any binary string} can be solved in linear time on a multi-tape Turing machine, but necessarily requires quadratic time in the model of single-tape Turing machines. If we allow polynomial variations in running time, Cobham-Edmonds thesis states that "the time complexities in any two reasonable and general models of computation are polynomially related" (Goldreich 2008, Chapter 1.2). This forms the basis for the complexity class P, which is the set of decision problems solvable by a deterministic Turing machine within polynomial time. The corresponding set of function problems is FP.

Question: Concrete bounding of computation time frequently produces complexity classes contingent upon what?

Question: A multi-tape Turing machine requires what type of time for a solution?

Question: A language solved in quadratic time implies the use of what type of Turing machine?

Question: What thesis specifies that a polynomial relationship exists within time complexities in a computational model?

Question: Decision problems capable of being solved by a deterministic Turing machine while maintaining adherence to polynomial time belong to what class?


Context: Many important complexity classes can be defined by bounding the time or space used by the algorithm. Some important complexity classes of decision problems defined in this manner are the following:

Question: What are two examples of measurements are bound within algorithms to establish complexity classes?

Question: What function is used by algorithms to define measurements like time or space?

Question: Bounding of time and space or similar measurements is often used by algorithms to define what?


Context: Other important complexity classes include BPP, ZPP and RP, which are defined using probabilistic Turing machines; AC and NC, which are defined using Boolean circuits; and BQP and QMA, which are defined using quantum Turing machines. #P is an important complexity class of counting problems (not decision problems). Classes like IP and AM are defined using Interactive proof systems. ALL is the class of all decision problems.

Question: What are three examples of complexity classes associated with definitions established by probabilistic Turing machines?

Question: AC and NC are complexity classes typically associated with what type of circuit?

Question: BQP and QMA are examples of complexity classes most commonly associated with what type of Turing machine?

Question: What is the expression used to represent a complexity class of counting problems?

Question: IP and AM are most commonly defined by what type of proof system?


Context: For the complexity classes defined in this way, it is desirable to prove that relaxing the requirements on (say) computation time indeed defines a bigger set of problems. In particular, although DTIME(n) is contained in DTIME(n2), it would be interesting to know if the inclusion is strict. For time and space requirements, the answer to such questions is given by the time and space hierarchy theorems respectively. They are called hierarchy theorems because they induce a proper hierarchy on the classes defined by constraining the respective resources. Thus there are pairs of complexity classes such that one is properly included in the other. Having deduced such proper set inclusions, we can proceed to make quantitative statements about how much more additional time or space is needed in order to increase the number of problems that can be solved.

Question: What is an example of a measurement within a complexity class that would create a bigger set of problems if the bounds were relaxed?

Question: In what expression can one expect to find DTIME(n)

Question: What theorems are responsible for determining questions of time and space requirements?

Question: Resources are constrained by hierarchy theorems to produce what?

Question: What kind of statement is made in the effort of establishing the time and space requirements needed to enhance the ultimate number of problems solved?


Context: The time and space hierarchy theorems form the basis for most separation results of complexity classes. For instance, the time hierarchy theorem tells us that P is strictly contained in EXPTIME, and the space hierarchy theorem tells us that L is strictly contained in PSPACE.

Question: What is the foundation for separation results within complexity classes?

Question: What is responsible for constraining P according to the time hierarchy theorem?

Question: Within what variable is L constrained according to the space hierarchy theorem?


Context: Many complexity classes are defined using the concept of a reduction. A reduction is a transformation of one problem into another problem. It captures the informal notion of a problem being at least as difficult as another problem. For instance, if a problem X can be solved using an algorithm for Y, X is no more difficult than Y, and we say that X reduces to Y. There are many different types of reductions, based on the method of reduction, such as Cook reductions, Karp reductions and Levin reductions, and the bound on the complexity of reductions, such as polynomial-time reductions or log-space reductions.

Question: What concept is frequently used to define complexity classes?

Question: Reduction essentially takes one problem and converts into what?

Question: According to reduction, if X and Y can be solved by the same algorithm then X performs what function in relationship to Y?

Question: What are two examples of different types of reduction?

Question: Polynomial time reductions are an example of what?


Context: The most commonly used reduction is a polynomial-time reduction. This means that the reduction process takes polynomial time. For example, the problem of squaring an integer can be reduced to the problem of multiplying two integers. This means an algorithm for multiplying two integers can be used to square an integer. Indeed, this can be done by giving the same input to both inputs of the multiplication algorithm. Thus we see that squaring is not more difficult than multiplication, since squaring can be reduced to multiplication.

Question: What is the most frequently employed type of reduction?

Question: What equates to a squared integer according to polynomial time reduction?

Question: What measurement of time is used in polynomial time reduction?

Question: What would need to remain constant in a multiplication algorithm to produce the same outcome whether multiplying or squaring two integers?

Question: According to polynomial time reduction squaring can ultimately be logically reduced to what?


Context: This motivates the concept of a problem being hard for a complexity class. A problem X is hard for a class of problems C if every problem in C can be reduced to X. Thus no problem in C is harder than X, since an algorithm for X allows us to solve any problem in C. Of course, the notion of hard problems depends on the type of reduction being used. For complexity classes larger than P, polynomial-time reductions are commonly used. In particular, the set of problems that are hard for NP is the set of NP-hard problems.

Question: The complexity of problems often depends on what?

Question: What would create a conflict between a problem X and problem C within the context of reduction?

Question: An algorithm for X which reduces to C would us to do what?

Question: A problem set that that is hard for the expression NP can also be stated how?


Context: If a problem X is in C and hard for C, then X is said to be complete for C. This means that X is the hardest problem in C. (Since many problems could be equally hard, one might say that X is one of the hardest problems in C.) Thus the class of NP-complete problems contains the most difficult problems in NP, in the sense that they are the ones most likely not to be in P. Because the problem P = NP is not solved, being able to reduce a known NP-complete problem, Π2, to another problem, Π1, would indicate that there is no known polynomial-time solution for Π1. This is because a polynomial-time solution to Π1 would yield a polynomial-time solution to Π2. Similarly, because all NP problems can be reduced to the set, finding an NP-complete problem that can be solved in polynomial time would mean that P = NP.

Question: The hardest problems in NP can be analogously written as what class of problems?

Question: NP complete problems contain the lowest likelihood of being located in what problem class?

Question: If P = NP is unsolved, and reduction is applied to a known NP-complete problem vis a vis Π2 to  Π1, what conclusion can be drawn for Π1?

Question: If polynomial time can be utilized within an NP-complete problem, what does the imply P is equal to?


Context: The complexity class P is often seen as a mathematical abstraction modeling those computational tasks that admit an efficient algorithm. This hypothesis is called the Cobham–Edmonds thesis. The complexity class NP, on the other hand, contains many problems that people would like to solve efficiently, but for which no efficient algorithm is known, such as the Boolean satisfiability problem, the Hamiltonian path problem and the vertex cover problem. Since deterministic Turing machines are special non-deterministic Turing machines, it is easily observed that each problem in P is also member of the class NP.

Question: What complexity class is characterized by a computational tasks and efficient algorithms?

Question: What hypothesis is associated with the complexity class of P viewed as a mathematical abstraction with efficient algorithmic functionality?

Question: What complexity class is commonly characterized by unknown algorithms to enhance solvability?

Question: What is an example of a problem that rests within the NP complexity class?

Question: In what theoretical machine is it confirmed that a problem in P belies membership in the NP class?


Context: The question of whether P equals NP is one of the most important open questions in theoretical computer science because of the wide implications of a solution. If the answer is yes, many important problems can be shown to have more efficient solutions. These include various types of integer programming problems in operations research, many problems in logistics, protein structure prediction in biology, and the ability to find formal proofs of pure mathematics theorems. The P versus NP problem is one of the Millennium Prize Problems proposed by the Clay Mathematics Institute. There is a US$1,000,000 prize for resolving the problem.

Question: If P is ultimately proven to be equal tot NP, what effect would this have on the efficiency of problems?

Question: What is a particular problem in biology that would benefit from determining that P = NP?

Question: What is the prize offered for finding a solution to P=NP?


Context: It was shown by Ladner that if P ≠ NP then there exist problems in NP that are neither in P nor NP-complete. Such problems are called NP-intermediate problems. The graph isomorphism problem, the discrete logarithm problem and the integer factorization problem are examples of problems believed to be NP-intermediate. They are some of the very few NP problems not known to be in P or to be NP-complete.

Question: Who demonstrated that P= NP implies problems not present in P or NP-complete?

Question: What is the name for a problem that meets Ladner's assertion?

Question: What is an example of an NP-intermediate problem not known to exist in P or NP-complete?


Context: The graph isomorphism problem is the computational problem of determining whether two finite graphs are isomorphic. An important unsolved problem in complexity theory is whether the graph isomorphism problem is in P, NP-complete, or NP-intermediate. The answer is not known, but it is believed that the problem is at least not NP-complete. If graph isomorphism is NP-complete, the polynomial time hierarchy collapses to its second level. Since it is widely believed that the polynomial hierarchy does not collapse to any finite level, it is believed that graph isomorphism is not NP-complete. The best algorithm for this problem, due to Laszlo Babai and Eugene Luks has run time 2O(√(n log(n))) for graphs with n vertices.

Question: What is the problem attributed to defining if two finite graphs are isomorphic?

Question: What class is most commonly not ascribed to the graph isomorphism problem in spite of definitive determination?

Question: What finite hierarchy implies that the graph isomorphism problem is NP-complete?

Question: To what level would the polynomial time hierarchy collapse if graph isomorphism is NP-complete?

Question: Who are commonly associated with the algorithm typically considered the most effective with respect to finite polynomial hierarchy and graph isomorphism?


Context: The integer factorization problem is the computational problem of determining the prime factorization of a given integer. Phrased as a decision problem, it is the problem of deciding whether the input has a factor less than k. No efficient integer factorization algorithm is known, and this fact forms the basis of several modern cryptographic systems, such as the RSA algorithm. The integer factorization problem is in NP and in co-NP (and even in UP and co-UP). If the problem is NP-complete, the polynomial time hierarchy will collapse to its first level (i.e., NP will equal co-NP). The best known algorithm for integer factorization is the general number field sieve, which takes time O(e(64/9)1/3(n.log 2)1/3(log (n.log 2))2/3) to factor an n-bit integer. However, the best known quantum algorithm for this problem, Shor's algorithm, does run in polynomial time. Unfortunately, this fact doesn't say much about where the problem lies with respect to non-quantum complexity classes.

Question: What computational problem is commonly associated with prime factorization?

Question: The integer factorization problem essentially seeks to determine if the value of of an input is less than what variable?

Question: That there currently exists no known integer factorization problem underpins what commonly used system?

Question: What is the most well-known algorithm associated with the integer factorization problem?


Context: Many known complexity classes are suspected to be unequal, but this has not been proved. For instance P ⊆ NP ⊆ PP ⊆ PSPACE, but it is possible that P = PSPACE. If P is not equal to NP, then P is not equal to PSPACE either. Since there are many known complexity classes between P and PSPACE, such as RP, BPP, PP, BQP, MA, PH, etc., it is possible that all these complexity classes collapse to one class. Proving that any of these classes are unequal would be a major breakthrough in complexity theory.

Question: What is the unproven assumption generally ascribed to the value of complexity classes?

Question: What is an expression that can be used to illustrate the suspected inequality of complexity classes?

Question: Where can the complexity classes RP, BPP, PP, BQP, MA, and PH be located?

Question: What evidence between and among complexity classes would signify a theoretical watershed for complexity theory?


Context: Along the same lines, co-NP is the class containing the complement problems (i.e. problems with the yes/no answers reversed) of NP problems. It is believed that NP is not equal to co-NP; however, it has not yet been proven. It has been shown that if these two complexity classes are not equal then P is not equal to NP.

Question: In what complexity class do complement problems of NP problems exist?

Question: How do the yes/no answers of a complement problem of NP appear?

Question: What is commonly believed to be the value relationship between P and co-NP

Question: What implication can be derived for P and NP if P and co-NP are established to be unequal?


Context: Similarly, it is not known if L (the set of all problems that can be solved in logarithmic space) is strictly contained in P or equal to P. Again, there are many complexity classes between the two, such as NL and NC, and it is not known if they are distinct or equal classes.

Question: What variable is associated with all problems solved within logarithmic space?

Question: Though unkown, what are the most commonly ascribed attributes of L in relation to P

Question: What lies between L and P that prevents a definitive determination of the relationship between L and P?

Question: What are two complexity classes between L and P?

Question: What is unknown about the complexity classes between L and P that further prevents determining the value relationship between L and P?


Context: Problems that can be solved in theory (e.g., given large but finite time), but which in practice take too long for their solutions to be useful, are known as intractable problems. In complexity theory, problems that lack polynomial-time solutions are considered to be intractable for more than the smallest inputs. In fact, the Cobham–Edmonds thesis states that only those problems that can be solved in polynomial time can be feasibly computed on some computational device. Problems that are known to be intractable in this sense include those that are EXPTIME-hard. If NP is not the same as P, then the NP-complete problems are also intractable in this sense. To see why exponential-time algorithms might be unusable in practice, consider a program that makes 2n operations before halting. For small n, say 100, and assuming for the sake of example that the computer does 1012 operations each second, the program would run for about 4 × 1010 years, which is the same order of magnitude as the age of the universe. Even with a much faster computer, the program would only be useful for very small instances and in that sense the intractability of a problem is somewhat independent of technological progress. Nevertheless, a polynomial time algorithm is not always practical. If its running time is, say, n15, it is unreasonable to consider it efficient and it is still useless except on small instances.

Question: Problems capable of theoretical solutions but consuming unreasonable time in practical application are known as what?

Question: Intractable problems lacking polynomial time solutions necessarily negate the practical efficacy of what type of algorithm?

Question: If NP is not equal to P, viewed through this lens, what type of problems can also be considered intractable?


Context: What intractability means in practice is open to debate. Saying that a problem is not in P does not imply that all large cases of the problem are hard or even that most of them are. For example, the decision problem in Presburger arithmetic has been shown not to be in P, yet algorithms have been written that solve the problem in reasonable times in most cases. Similarly, algorithms can solve the NP-complete knapsack problem over a wide range of sizes in less than quadratic time and SAT solvers routinely handle large instances of the NP-complete Boolean satisfiability problem.

Question: What eponymous variation of arithmetic presents a decision problem not evidenced in P?

Question: Despite the Presburger problem, and in view of intractability, what has been done to establish solutions in reasonable periods of time?

Question: What is an example of a problem to which effective algorithms have provided a solution in spite of the intractability associated with the breadth of sizes?

Question: How quickly can an algorithm solve an NP-complete knapsack problem?

Question: What is the example of another problem characterized by large instances that is routinely solved by SAT handlers employing efficient algorithms?


Context: Before the actual research explicitly devoted to the complexity of algorithmic problems started off, numerous foundations were laid out by various researchers. Most influential among these was the definition of Turing machines by Alan Turing in 1936, which turned out to be a very robust and flexible simplification of a computer.

Question: What tactic did researchers employ to offset the former deficit of work surrounding the complexity of algorithmic problems?

Question: Who was the most influential researcher among those grappling with the deficit of work surrounding the complexity posed by algorithmic problems?

Question: What theoretical device is attributed to Alan Turing?

Question: In what year was the Alan Turing's definitional model of a computing device received?

Question: In the most basic sense what did a Turing machine emulate?


Context: As Fortnow & Homer (2003) point out, the beginning of systematic studies in computational complexity is attributed to the seminal paper "On the Computational Complexity of Algorithms" by Juris Hartmanis and Richard Stearns (1965), which laid out the definitions of time and space complexity and proved the hierarchy theorems. Also, in 1965 Edmonds defined a "good" algorithm as one with running time bounded by a polynomial of the input size.

Question: What paper is commonly considered the bellwether ushering in systematic studies computational complexity?

Question: What individuals were responsible for authoring "On the Computational Complexity of Algorithms"?

Question: In what year was Hatmanis and Stearn's seminal work in computational complexity received?

Question: What complex measurements were defined by "On the Computational Complexity of Algorithms"?

Question: In what year did Edmond's characterize a "good" algorithm?


Context: Earlier papers studying problems solvable by Turing machines with specific bounded resources include  John Myhill's definition of linear bounded automata (Myhill 1960), Raymond Smullyan's study of rudimentary sets (1961), as well as Hisao Yamada's paper on real-time computations (1962). Somewhat earlier, Boris Trakhtenbrot (1956), a pioneer in the field from the USSR, studied another specific complexity measure. As he remembers:

Question: Who provided a definition of linear bounded automata in 1960?

Question: In what year did Raymond Sullivan publish a study of rudimentary sets?

Question: In 1962, who was responsible for the authorship of a paper published on real time-computations?


Context: Even though some proofs of complexity-theoretic theorems regularly assume some concrete choice of input encoding, one tries to keep the discussion abstract enough to be independent of the choice of encoding. This can be achieved by ensuring that different representations can be transformed into each other efficiently.

Question: What is the concrete choice typically assumed by most complexity-theoretic theorems?

Question: In the effort of maintaining a level of abstraction, what choice is typically left independent?


Context: In 1967, Manuel Blum developed an axiomatic complexity theory based on his axioms and proved an important result, the so-called, speed-up theorem. The field really began to flourish in 1971 when the US researcher Stephen Cook and, working independently, Leonid Levin in the USSR, proved that there exist practically relevant problems that are NP-complete. In 1972, Richard Karp took this idea a leap forward with his landmark paper, "Reducibility Among Combinatorial Problems", in which he showed that 21 diverse combinatorial and graph theoretical problems, each infamous for its computational intractability, are NP-complete.

Question: Who is responsible for axiomatic complexity theory?

Question: What theorem was implicated by Manuel Blum's axioms?

Question: What is the paper written by Richard Karp in 1972 that ushered in a new era of understanding between intractability and NP-complete problems?

Question: How many combinatory and graph theoretical problems, formerly believed to be plagued by intractability, did Karp's paper address?


Context: The role of teacher is often formal and ongoing, carried out at a school or other place of formal education. In many countries, a person who wishes to become a teacher must first obtain specified professional qualifications or credentials from a university or college. These professional qualifications may include the study of pedagogy, the science of teaching. Teachers, like other professionals, may have to continue their education after they qualify, a process known as continuing professional development. Teachers may use a lesson plan to facilitate student learning, providing a course of study which is called the curriculum.

Question: What is a course of study called?

Question: What is another name to describe the science of teaching?

Question: Where do most teachers get their credentials from?

Question: What can a teacher use to help students learn?

Question: Where is a teacher most likely to be teaching at?


Context: A teacher's role may vary among cultures. Teachers may provide instruction in literacy and numeracy, craftsmanship or vocational training, the arts, religion, civics, community roles, or life skills.

Question: What factor may make a teacher's role vary?

Question: What is similar to literacy that a teacher would teach?

Question: What is similar to vocational training?

Question: What type of teaching would help the most with everyday life?


Context: In some countries, formal education can take place through home schooling. Informal learning may be assisted by a teacher occupying a transient or ongoing role, such as a family member, or by anyone with knowledge or skills in the wider community setting.

Question: Who is most likely to teach a child at home?

Question: If someone is being taught at their place of residence, what is it called?

Question: What type of education is home schooling?

Question: What is the opposite of an ongoing role of teaching?

Question: What would a community member need to teach informally?


Context: Religious and spiritual teachers, such as gurus, mullahs, rabbis, pastors/youth pastors and lamas, may teach religious texts such as the Quran, Torah or Bible.

Question: What is another general name for a religious teacher?

Question: What type of text is the Quran?

Question: Name a text that might be used by a religious teacher to teach.

Question: Who would generally teach from a work like the Quran, Torah or Bible?


Context: Teaching may be carried out informally, within the family, which is called homeschooling, or in the wider community. Formal teaching may be carried out by paid professionals. Such professionals enjoy a status in some societies on a par with physicians, lawyers, engineers, and accountants (Chartered or CPA).

Question: What is another name for teaching within the family?

Question: Who is most likely to be doing formal teaching?

Question: What is another type of accountant other than a CPA?

Question: In addition to teaching within the family, where else is informal teaching done?

Question: Who is generally considered on the same level as physicians, lawyers, engineers, and accountants (Chartered or CPA)?


Context: A teacher's professional duties may extend beyond formal teaching. Outside of the classroom teachers may accompany students on field trips, supervise study halls, help with the organization of school functions, and serve as supervisors for extracurricular activities. In some education systems, teachers may have responsibility for student discipline.

Question: What could a teacher help in organizing?

Question: What would type of activities would require a teacher to take on a supervisor role?

Question: What would type of studying would require a teacher to take on a supervisor role?

Question: Who might be responsible for student discipline?


Context: There are a variety of bodies designed to instill, preserve and update the knowledge and professional standing of teachers. Around the world many governments operate teacher's colleges, which are generally established to serve and protect the public interest through certifying, governing and enforcing the standards of practice for the teaching profession.

Question: What do government's run that affects teachers?

Question: Why would a teacher's college exist?

Question: Who would a teacher's college be protecting?

Question: Who is taught at teacher's colleges?

Question: Teacher's colleges certify, govern and enforce what for teachers?


Context: The functions of the teacher's colleges may include setting out clear standards of practice, providing for the ongoing education of teachers, investigating complaints involving members, conducting hearings into allegations of professional misconduct and taking appropriate disciplinary action and accrediting teacher education programs. In many situations teachers in publicly funded schools must be members in good standing with the college, and private schools may also require their teachers to be college peoples. In other areas these roles may belong to the State Board of Education, the Superintendent of Public Instruction, the State Education Agency or other governmental bodies. In still other areas Teaching Unions may be responsible for some or all of these duties.

Question: Who could be the subjects of a teacher's college investigation?

Question: What could someone be investigated for?

Question: A teacher must be a member in good standing with what entity in many situations?

Question: Who might take disciplinary action against a teacher?

Question: Who might be charged with accrediting teacher education programs?


Context: In education, teachers facilitate student learning, often in a school or academy or perhaps in another environment such as outdoors. A teacher who teaches on an individual basis may be described as a tutor.

Question: Where might a teacher teach that is not inside a building?

Question: What is the name for a teacher of just one person?

Question: Where is another indoor location for a teacher other than a school?

Question: What is the role of teachers in education?


Context: The objective is typically accomplished through either an informal or formal approach to learning, including a course of study and lesson plan that teaches skills, knowledge and/or thinking skills. Different ways to teach are often referred to as pedagogy. When deciding what teaching method to use teachers consider students' background knowledge, environment, and their learning goals as well as standardized curricula as determined by the relevant authority. Many times, teachers assist in learning outside of the classroom by accompanying students on field trips. The increasing use of technology, specifically the rise of the internet over the past decade, has begun to shape the way teachers approach their roles in the classroom.

Question: What is another way of learning that is not formal?

Question: What is a name for the different ways to instruct students?

Question: What is a typical name for learning that is not inside of the classroom?

Question: What has started to change the way teachers teach in the classroom, generally?

Question: What has risen to be a factor in teaching today?


Context: The objective is typically a course of study, lesson plan, or a practical skill. A teacher may follow standardized curricula as determined by the relevant authority. The teacher may interact with students of different ages, from infants to adults, students with different abilities and students with learning disabilities.

Question: What objective would be labeled as practical?

Question: Who would decide on the curricula that is consistent and standard?

Question: What type of disability would a teacher help a student with?

Question: What is the youngest student a teacher might have?

Question: What type of curricula is it that a teacher may likely follow?


Context: Teaching using pedagogy also involve assessing the educational levels of the students on particular skills. Understanding the pedagogy of the students in a classroom involves using differentiated instruction as well as supervision to meet the needs of all students in the classroom. Pedagogy can be thought of in two manners. First, teaching itself can be taught in many different ways, hence, using a pedagogy of teaching styles. Second, the pedagogy of the learners comes into play when a teacher assesses the pedagogic diversity of his/her students and differentiates for the individual students accordingly. For example, an experienced teacher and parent described the place of a teacher in learning as follows: "The real bulk of learning takes place in self-study and problem solving with a lot of feedback around that loop. The function of the teacher is to pressure the lazy, inspire the bored, deflate the cocky, encourage the timid, detect and correct individual flaws, and broaden the viewpoint of all. This function looks like that of a coach using the whole gamut of psychology to get each new class of rookies off the bench and into the game."

Question: What would a teacher assess the levels of a student on?

Question: In what areas does most of the learning take place?

Question: What would a teacher do for someone who is timid?

Question: What would a teacher do for someone who is cocky?

Question: What function is a teacher's role similar to?


Context: Perhaps the most significant difference between primary school and secondary school teaching is the relationship between teachers and children. In primary schools each class has a teacher who stays with them for most of the week and will teach them the whole curriculum. In secondary schools they will be taught by different subject specialists each session during the week and may have ten or more different teachers. The relationship between children and their teachers tends to be closer in the primary school where they act as form tutor, specialist teacher and surrogate parent during the course of the day.

Question: What's the biggest difference in the teaching relationship for primary and secondary school?

Question: What does a teacher teach in primary school?

Question: Who teaches in secondary schools?

Question: Which type of school has a closer teacher-child relationship?

Question: What type of parenting role does a teacher take on?


Context: This is true throughout most of the United States as well. However, alternative approaches for primary education do exist. One of these, sometimes referred to as a "platoon" system, involves placing a group of students together in one class that moves from one specialist to another for every subject. The advantage here is that students learn from teachers who specialize in one subject and who tend to be more knowledgeable in that one area than a teacher who teaches many subjects. Students still derive a strong sense of security by staying with the same group of peers for all classes.

Question: What type of approaches for primary school are available that are different than the norm?

Question: What type of system takes students from one specialist to another for each subject?

Question: In a platoon style teaching, what gives the children security?

Question: The teacher's in a platoon style teaching are usually more _____?

Question: What country is this teaching subject discussing?


Context: Co-teaching has also become a new trend amongst educational institutions. Co-teaching is defined as two or more teachers working harmoniously to fulfill the needs of every student in the classroom. Co-teaching focuses the student on learning by providing a social networking support that allows them to reach their full cognitive potential. Co-teachers work in sync with one another to create a climate of learning.

Question: What is a new trend in teaching?

Question: How many teachers are usually involved when co-teaching?

Question: What does co-teaching get the students to focus on?

Question: How do co-teachers work with each other to fulfill the needs of students?

Question: What type of support does co-teaching provide?


Context: Throughout the history of education the most common form of school discipline was corporal punishment. While a child was in school, a teacher was expected to act as a substitute parent, with all the normal forms of parental discipline open to them.

Question: What was the usual type of school discipline?

Question: What was the teacher's role while the child was with them?

Question: What types of discipline did the teacher have access to?

Question: How common of a type was corporal punishment in schools?

Question: When could a teacher act in the role of a parent?


Context: In past times, corporal punishment (spanking or paddling or caning or strapping or birching the student in order to cause physical pain) was one of the most common forms of school discipline throughout much of the world. Most Western countries, and some others, have now banned it, but it remains lawful in the United States following a US Supreme Court decision in 1977 which held that paddling did not violate the US Constitution.

Question: How common was the form of corporal punishment in the past?

Question: Where is corporal punishment no longer practiced?

Question: What Western country is corporal punishment still allowed?

Question: What U.S. entity said that corporal punishment was Constitutional?

Question: What does corporal punishment cause a student?


Context: 30 US states have banned corporal punishment, the others (mostly in the South) have not. It is still used to a significant (though declining) degree in some public schools in Alabama, Arkansas, Georgia, Louisiana, Mississippi, Oklahoma, Tennessee and Texas. Private schools in these and most other states may also use it. Corporal punishment in American schools is administered to the seat of the student's trousers or skirt with a specially made wooden paddle. This often used to take place in the classroom or hallway, but nowadays the punishment is usually given privately in the principal's office.

Question: How many US states do no practice corporal punishment?

Question: Where is corporal punishment practiced the most?

Question: Is corporal punishment increasing or declining in the South?

Question: What tool is used in corporal punishment?

Question: Where is corporal punishment usually performed these days?


Context: Official corporal punishment, often by caning, remains commonplace in schools in some Asian, African and Caribbean countries. For details of individual countries see School corporal punishment.

Question: What is a common practice in official corporal punishment?

Question: What countries is corporal punishment still a normal practice?

Question: Where can you find more information on a country's practices?


Context: Currently detention is one of the most common punishments in schools in the United States, the UK, Ireland, Singapore and other countries. It requires the pupil to remain in school at a given time in the school day (such as lunch, recess or after school); or even to attend school on a non-school day, e.g. "Saturday detention" held at some schools. During detention, students normally have to sit in a classroom and do work, write lines or a punishment essay, or sit quietly.

Question: What is a common punishment in Singapore?

Question: What is a common punishment in the UK and Ireland?

Question: Where does the pupil remain while in detention

Question: If a detention requires a pupil to just sit there, how are they required to sit?

Question: If a detention asks the student to write, what do they write?