romikgosai/squad_modified_2 huggingface dataset:
In lossy audio compression, methods of psychoacoustics are used to remove non-audible (or less audible) components of the audio signal. Compression of human speech is often performed with even more specialized techniques; speech coding, or voice coding, is sometimes distinguished as a separate discipline from audio compression. Different audio and speech compression standards are listed under audio coding formats. Voice compression is used in internet telephony, for example, audio compression is used for CD ripping and is decoded by the audio players.

Q1: What is used for CD ripping?
A1: audio compression
Q2: What methods are used to remove non-audible components of audio signals?
A2: psychoacoustics
Q3: What compression is usually performed with even more specialized techniques?
A3: human speech
Q4: What is used in internet telephony?
A4: Voice compression
Q5: What is encoded by audio players?
A5: audio compression
There is a close connection between machine learning and compression: a system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution) while an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence."

Q1: What is Machine learning closely related to?
A1: compression
Q2: What type of compressor can be used for prediction?
A2: optimal
Q3: What has been used as a benchmark for "general intelligence"?
A3: data compression
Data compression can be viewed as a special case of data differencing: Data differencing consists of producing a difference given a source and a target, with patching producing a target given a source and a difference, while data compression consists of producing a compressed file given a target, and decompression consists of producing a target given only a compressed file. Thus, one can consider data compression as data differencing with empty source data, the compressed file corresponding to a "difference from nothing." This is the same as considering absolute entropy (corresponding to data compression) as a special case of relative entropy (corresponding to data differencing) with no initial data.

Q1: What can be looked at as a special case of data differencing?
A1: Data compression
Q2: What is made up of producing a difference given a source and a target?
A2: Data differencing
Q3: What can classified as data differencing with empty source data?
A3: data compression
Audio data compression, not to be confused with dynamic range compression, has the potential to reduce the transmission bandwidth and storage requirements of audio data. Audio compression algorithms are implemented in software as audio codecs. Lossy audio compression algorithms provide higher compression at the cost of fidelity and are used in numerous audio applications. These algorithms almost all rely on psychoacoustics to eliminate less audible or meaningful sounds, thereby reducing the space required to store or transmit them.

Q1: What has the potential to reduce the transmission bandwidth and storage requirements of audio data?
A1: Audio data compression
Q2: What is inserted in software as audio codecs?
A2: Audio compression algorithms
Q3: What compression algorithms provide higher compression at the cost of fidelity?
A3: Lossy
Lossless audio compression produces a representation of digital data that decompress to an exact digital duplicate of the original audio stream, unlike playback from lossy compression techniques such as Vorbis and MP3. Compression ratios are around 50–60% of original size, which is similar to those for generic lossless data compression. Lossless compression is unable to attain high compression ratios due to the complexity of waveforms and the rapid changes in sound forms. Codecs like FLAC, Shorten and TTA use linear prediction to estimate the spectrum of the signal. Many of these algorithms use convolution with the filter [-1 1] to slightly whiten or flatten the spectrum, thereby allowing traditional lossless compression to work more efficiently. The process is reversed upon decompression.

Q1: What produces a representation of digital data that decompresses?
A1: Lossless audio compression
Q2: What compression cannot attain high compression ratios?
A2: Lossless
Q3: What are around 50-60% of the original size?
A3: Compression ratios
Lossy audio compression is used in a wide range of applications. In addition to the direct applications (mp3 players or computers), digitally compressed audio streams are used in most video DVDs, digital television, streaming media on the internet, satellite and cable radio, and increasingly in terrestrial radio broadcasts. Lossy compression typically achieves far greater compression than lossless compression (data of 5 percent to 20 percent of the original stream, rather than 50 percent to 60 percent), by discarding less-critical data.

Q1: What compression is used in a lot of applications?
A1: Lossy
Q2: What is used in most video DVDs?
A2: digitally compressed audio streams
Q3: What compression usually achieves far greater compression then lossless?
A3: Lossy
To determine what information in an audio signal is perceptually irrelevant, most lossy compression algorithms use transforms such as the modified discrete cosine transform (MDCT) to convert time domain sampled waveforms into a transform domain. Once transformed, typically into the frequency domain, component frequencies can be allocated bits according to how audible they are. Audibility of spectral components calculated using the absolute threshold of hearing and the principles of simultaneous masking—the phenomenon wherein a signal is masked by another signal separated by frequency—and, in some cases, temporal masking—where a signal is masked by another signal separated by time. Equal-loudness contours may also be used to weight the perceptual importance of components. Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models.

Q1: What may be used to weight the importance of components?
A1: Equal-loudness contours
Q2: What are often called psychoacoustic models?
A2: Models of the human ear-brain
Q3: What is the process called when a signal is masked by another signal separated by time?
A3: temporal masking
Other types of lossy compressors, such as the linear predictive coding (LPC) used with speech, are source-based coders. These coders use a model of the sound's generator (such as the human vocal tract with LPC) to whiten the audio signal (i.e., flatten its spectrum) before quantization. LPC may be thought of as a basic perceptual coding technique: reconstruction of an audio signal using a linear predictor shapes the coder's quantization noise into the spectrum of the target signal, partially masking it.

Q1: What is the coding called that is used with speech?
A1: linear predictive coding
Q2: What is thought of as a basic perceptual coding technique?
A2: linear predictive coding
Q3: What uses a model of the sound's generator?
A3: source-based coders
Latency results from the methods used to encode and decode the data. Some codecs will analyze a longer segment of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time to decode. (Often codecs create segments called a "frame" to create discrete data segments for encoding and decoding.) The inherent latency of the coding algorithm can be critical; for example, when there is a two-way transmission of data, such as with a telephone conversation, significant delays may seriously degrade the perceived quality.

Q1: What results from methods used to encode and decode data?
A1: Latency
Q2: What analyzes a statement of data to boost performance?
A2: codecs
Q3: What creates segments called a "frame"?
A3: codecs
In contrast to the speed of compression, which is proportional to the number of operations required by the algorithm, here latency refers to the number of samples that must be analysed before a block of audio is processed. In the minimum case, latency is zero samples (e.g., if the coder/decoder simply reduces the number of bits used to quantize the signal). Time domain algorithms such as LPC also often have low latencies, hence their popularity in speech coding for telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed to implement a psychoacoustic model in the frequency domain, and latency is on the order of 23 ms (46 ms for two-way communication)).

Q1: What refers to the number of samples that must be analysed before a block of audio is processed?
A1: latency
Q2: What has zero samples in a minimum case?
A2: latency
Q3: What often has low latencies?
A3: LPC
If the data to be compressed is analog (such as a voltage that varies with time), quantization is employed to digitize it into numbers (normally integers). This is referred to as analog-to-digital (A/D) conversion. If the integers generated by quantization are 8 bits each, then the entire range of the analog signal is divided into 256 intervals and all the signal values within an interval are quantized to the same number. If 16-bit integers are generated, then the range of the analog signal is divided into 65,536 intervals.

Q1: What varies with time?
A1: voltage
Q2: What has to be compressed in order to perform properly?
A2: data
Q3: What does "A/D" stand for?
A3: analog-to-digital
A literature compendium for a large variety of audio coding systems was published in the IEEE Journal on Selected Areas in Communications (JSAC), February 1988. While there were some papers from before that time, this collection documented an entire variety of finished, working audio coders, nearly all of them using perceptual (i.e. masking) techniques and some kind of frequency analysis and back-end noiseless coding. Several of these papers remarked on the difficulty of obtaining good, clean digital audio for research purposes. Most, if not all, of the authors in the JSAC edition were also active in the MPEG-1 Audio committee.

Q1: What was published in the IEEE Journal in 1988?
A1: literature compendium
Q2: What did the literature compendium document?
A2: finished, working audio coders
Q3: Where were most of the authors in the JSAC edition?
A3: MPEG-1 Audio committee
The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an engineering professor at the University of Buenos Aires. In 1983, using the psychoacoustic principle of the masking of critical bands first published in 1967, he started developing a practical application based on the recently developed IBM PC computer, and the broadcast automation system was launched in 1987 under the name Audicom. Twenty years later, almost all the radio stations in the world were using similar technology manufactured by a number of companies.

Q1: Who developed the first commercial broadcast automation audio compression system?
A1: Oscar Bonello
Q2: Who was an engineering professor at the University of Buenos Aires?
A2: Oscar Bonello
Q3: What was launched in 1987 under the name Audicom?
A3: broadcast automation system
The majority of video compression algorithms use lossy compression. Uncompressed video requires a very high data rate. Although lossless video compression codecs perform at a compression factor of 5-12, a typical MPEG-4 lossy compression video has a compression factor between 20 and 200. As in all lossy compression, there is a trade-off between video quality, cost of processing the compression and decompression, and system requirements. Highly compressed video may present visible or distracting artifacts.

Q1: What all mostly use lossy compression?
A1: video compression algorithms
Q2: What requires a high data rate?
A2: Uncompressed video
Q3: What may present visible or distracting artifacts?
A3: Highly compressed video
Some video compression schemes typically operate on square-shaped groups of neighboring pixels, often called macroblocks. These pixel groups or blocks of pixels are compared from one frame to the next, and the video compression codec sends only the differences within those blocks. In areas of video with more motion, the compression must encode more data to keep up with the larger number of pixels that are changing. Commonly during explosions, flames, flocks of animals, and in some panning shots, the high-frequency detail leads to quality decreases or to increases in the variable bitrate.

Q1: What compression typically operate on square-shaped groups of pixels?
A1: video
Q2: What only sends the differences within those pixels and blocks?
A2: macroblocks
Q3: What does high-frequency detail lead to?
A3: variable bitrate
Video data may be represented as a series of still image frames. The sequence of frames contains spatial and temporal redundancy that video compression algorithms attempt to eliminate or code in a smaller size. Similarities can be encoded by only storing differences between frames, or by using perceptual features of human vision. For example, small differences in color are more difficult to perceive than are changes in brightness. Compression algorithms can average a color across these similar areas to reduce space, in a manner similar to those used in JPEG image compression. Some of these methods are inherently lossy while others may preserve all relevant information from the original, uncompressed video.

Q1: What may be represented as a series of still image frames?
A1: Video data
Q2: What can average a color across similar areas to reduce space?
A2: Compression algorithms
Q3: What is hard to tell with smaller differences involved?
A3: color
The most powerful used method works by comparing each frame in the video with the previous one. If the frame contains areas where nothing has moved, the system simply issues a short command that copies that part of the previous frame, bit-for-bit, into the next one. If sections of the frame move in a simple manner, the compressor emits a (slightly longer) command that tells the decompressor to shift, rotate, lighten, or darken the copy. This longer command still remains much shorter than intraframe compression. Interframe compression works well for programs that will simply be played back by the viewer, but can cause problems if the video sequence needs to be edited.

Q1: What still remains much shorter than intraframe compression?
A1: longer command
Q2: What happens when a frame contains areas where nothing has moved?
A2: the system simply issues a short command
Q3: What is a sequence that can be edited?
A3: video
Because interframe compression copies data from one frame to another, if the original frame is simply cut out (or lost in transmission), the following frames cannot be reconstructed properly. Some video formats, such as DV, compress each frame independently using intraframe compression. Making 'cuts' in intraframe-compressed video is almost as easy as editing uncompressed video: one finds the beginning and ending of each frame, and simply copies bit-for-bit each frame that one wants to keep, and discards the frames one doesn't want. Another difference between intraframe and interframe compression is that, with intraframe systems, each frame uses a similar amount of data. In most interframe systems, certain frames (such as "I frames" in MPEG-2) aren't allowed to copy data from other frames, so they require much more data than other frames nearby.

Q1: What copies data from one frame to another?
A1: interframe compression
Q2: What is a type of video format?
A2: DV
Q3: What is almost as easy as editing uncompressed video?
A3: Making 'cuts'
Today, nearly all commonly used video compression methods (e.g., those in standards approved by the ITU-T or ISO) apply a discrete cosine transform (DCT) for spatial redundancy reduction. The DCT that is widely used in this regard was introduced by N. Ahmed, T. Natarajan and K. R. Rao in 1974. Other methods, such as fractal compression, matching pursuit and the use of a discrete wavelet transform (DWT) have been the subject of some research, but are typically not used in practical products (except for the use of wavelet coding as still-image coders without motion compensation). Interest in fractal compression seems to be waning, due to recent theoretical analysis showing a comparative lack of effectiveness of such methods.

Q1: What do all commonly used video compression methods use?
A1: discrete cosine transform
Q2: What does a DCT do?
A2: spatial redundancy reduction
Q3: What is not used in practical products?
A3: fractal compression
Genetics compression algorithms are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and genetic algorithms adapted to the specific datatype. In 2012, a team of scientists from Johns Hopkins University published a genetic compression algorithm that does not use a reference genome for compression. HAPZIPPER was tailored for HapMap data and achieves over 20-fold compression (95% reduction in file size), providing 2- to 4-fold better compression and in much faster time than the leading general-purpose compression utilities. For this, Chanda, Elhaik, and Bader introduced MAF based encoding (MAFE), which reduces the heterogeneity of the dataset by sorting SNPs by their minor allele frequency, thus homogenizing the dataset. Other algorithms in 2009 and 2013 (DNAZip and GenomeZip) have compression ratios of up to 1200-fold—allowing 6 billion basepair diploid human genomes to be stored in 2.5 megabytes (relative to a reference genome or averaged over many genomes).

Q1: What are the latest generation of lossless algorithms?
A1: Genetics
Q2: What encoding reduces the heterogeneity of a dataset by sorting SNPs?
A2: MAFE
Q3: What two algorithms have compression ratios of up to 1200-fold?
A3: DNAZip and GenomeZip
The Sun had the largest circulation of any daily newspaper in the United Kingdom, but in late 2013 slipped to second largest Saturday newspaper behind the Daily Mail. It had an average daily circulation of 2.2 million copies in March 2014. Between July and December 2013 the paper had an average daily readership of approximately 5.5 million, with approximately 31% of those falling into the ABC1 demographic and 68% in the C2DE demographic. Approximately 41% of readers are women. The Sun has been involved in many controversies in its history, including its coverage of the 1989 Hillsborough football stadium disaster. Regional editions of the newspaper for Scotland, Northern Ireland and the Republic of Ireland are published in Glasgow (The Scottish Sun), Belfast (The Sun) and Dublin (The Irish Sun) respectively.

Q1: Which newspaper overtook The Sun as largest Saturday UK newspaper?
A1: Daily Mail
Q2: What was the sun's circulation per day in March 2014?
A2: 2.2 million copies
Q3: What was the largest demographic for readers of The Sun?
A3: C2DE demographic
Q4: What is the percentage of The Sun readers who are women?
A4: Approximately 41%
Q5: What is one controversial event covered by The Sun?
A5: 1989 Hillsborough football stadium disaster
On 26 February 2012, The Sun on Sunday was launched to replace the closed News of the World, employing some of its former journalists. In late 2013, it was given a new look, with a new typeface. The average circulation for The Sun on Sunday in March 2014 was 1,686,840; but in May 2015 The Mail on Sunday sold more copies for the first time, an average of 28,650 over those of its rival: 1,497,855 to 1,469,195. Roy Greenslade issued some caveats over the May 2015 figures, but believes the weekday Daily Mail will overtake The Sun in circulation during 2016.

Q1: Which newspaper did the The Sun on Sunday take the place of?
A1: News of the World
Q2: In what year was The Sun's look changed?
A2: 2013
Q3: What was the circulation of The Sun on Sunday in March 2014?
A3: 1,686,840
Q4: When did the Sunday Mail overtake The Sun in sales numbers?
A4: May 2015
Q5: When is the weekly Mail forecast to pass The Sun in terms of circulation?
A5: during 2016
Research commissioned by Cecil King from Mark Abrams of Sussex University, The Newspaper Reading Public of Tomorrow, identified demographic changes which suggested reasons why the Herald might be in decline. The new paper was intended to add a readership of 'social radicals' to the Herald's 'political radicals'. Launched with an advertising budget of £400,000 the brash new paper "burst forth with tremendous energy", according to The Times. Its initial print run of 3.5 million was attributed to 'curiosity' and the 'advantage of novelty', and had declined to the previous circulation of the Daily Herald (1.2 million) within a few weeks.

Q1: Who ordered research on the Herald?
A1: Cecil King from Mark Abrams of Sussex University
Q2: What was named as a potential reason for the Herald's decline?
A2: demographic changes
Q3: What type of people was the new paper supposed to attract?
A3: 'social radicals'
Q4: What was the advertising budget of the new paper?
A4: £400,000
Q5: What was the number of papers in the first print run of the Times?
A5: 3.5 million
Seizing the opportunity to increase his presence on Fleet Street, he made an agreement with the print unions, promising fewer redundancies if he acquired the newspaper. He assured IPC that he would publish a "straightforward, honest newspaper" which would continue to support Labour. IPC, under pressure from the unions, rejected Maxwell's offer, and Murdoch bought the paper for £800,000, to be paid in instalments. He would later remark: "I am constantly amazed at the ease with which I entered British newspapers."

Q1: What did the prospective owner of the paper promise?
A1: fewer redundancies
Q2: How did the prospective publisher describe the newspaper he would produce?
A2: a "straightforward, honest newspaper"
Q3: Which party would the newspaper endorse?
A3: Labour
Q4: What was the price paid for the newspaper?
A4: £800,000
Q5: Whose offer to buy the paper was turned down?
A5: Maxwell's offer
Murdoch found he had such a rapport with Larry Lamb over lunch that other potential recruits as editor were not interviewed and Lamb was appointed as the first editor of the new Sun. He was scathing in his opinion of the Mirror, where he had recently been employed as a senior sub-editor, and shared Murdoch's view that a paper's quality was best measured by its sales, and he regarded the Mirror as overstaffed, and primarily aimed at an ageing readership. Lamb hastily recruited a staff of about 125 reporters, who were mostly selected for their availability rather than their ability.

Q1: What position did Larry Lamb take with the Sun?
A1: editor
Q2: What type of view did Lamb have of the Mirror?
A2: scathing
Q3: What did Lamb and Murdoch believe to be the best indicator of newspaper quality?
A3: its sales
Q4: What position did Lamb formerly hold at the Mirror?
A4: senior sub-editor
Q5: How many reporters did Lamb hire for the Mirror's initial staff?
A5: about 125