derek-thomas/squad-v1.1-t5-question-generation huggingface dataset:
Cotton lisle is a finely-spun, tightly twisted type of cotton that is noted for being strong and durable. Lisle is composed of two strands that have each been twisted an extra twist per inch than ordinary yarns and combined to create a single thread. The yarn is spun so that it is compact and solid. This cotton is used mainly for underwear, stockings, and gloves. Colors applied to this yarn are noted for being more brilliant than colors applied to softer yarn. This type of thread was first made in the city of Lisle, France (now Lille), hence its name.

Question: What is a fine, tightly twisted type of cotton fabric? Question: What is lisle considered to be as a fabric? Question: What does extra twisting of lisle thread produce? Question: How is color perceived to be for lisle in comparison to softer yarns? Question: Where was the lisle thread originally made?
The largest producers of cotton, currently (2009), are China and India, with annual production of about 34 million bales and 33.4 million bales, respectively; most of this production is consumed by their respective textile industries. The largest exporters of raw cotton are the United States, with sales of $4.9 billion, and Africa, with sales of $2.1 billion. The total international trade is estimated to be $12 billion. Africa's share of the cotton trade has doubled since 1980. Neither area has a significant domestic textile industry, textile manufacturing having moved to developing nations in Eastern and South Asia such as India and China. In Africa, cotton is grown by numerous small holders. Dunavant Enterprises, based in Memphis, Tennessee, is the leading cotton broker in Africa, with hundreds of purchasing agents. It operates cotton gins in Uganda, Mozambique, and Zambia. In Zambia, it often offers loans for seed and expenses to the 180,000 small farmers who grow cotton for it, as well as advice on farming methods. Cargill also purchases cotton in Africa for export.

Question: In 2009 what were the largest producers of cotton? Question: How do the US and Africa rank as exporters of cotton? Question: What is the combined international trade in cotton? Question: What company is the top cotton broker in Africa? Question: Where is Dunavant Enterprises based?
The 25,000 cotton growers in the United States of America are heavily subsidized at the rate of $2 billion per year although China now provides the highest overall level of cotton sector support. The future of these subsidies is uncertain and has led to anticipatory expansion of cotton brokers' operations in Africa. Dunavant expanded in Africa by buying out local operations. This is only possible in former British colonies and Mozambique; former French colonies continue to maintain tight monopolies, inherited from their former colonialist masters, on cotton purchases at low fixed prices.

Question: How many subsidized cotton growers are in the US? Question: What is the rate of subsidies of cotton growers in the US? Question: What country provides the highest rate of support for cotton growers? Question: Where in Africa is it possible to buy out brokerages in cotton? Question: Which former colonies still have tight monopolies on cotton brokering?
While Brazil was fighting the US through the WTO's Dispute Settlement Mechanism against a heavily subsidized cotton industry, a group of four least-developed African countries – Benin, Burkina Faso, Chad, and Mali – also known as "Cotton-4" have been the leading protagonist for the reduction of US cotton subsidies through negotiations. The four introduced a "Sectoral Initiative in Favour of Cotton", presented by Burkina Faso's President Blaise Compaoré during the Trade Negotiations Committee on 10 June 2003.

Question: What organization has Brazil tried to use to curb US cotton subsidies? Question: What was Brazil's argument against the US cotton industry? Question: What are the African countries of Benin, Burkina Faso, Chad, and Mali referred to as in the cotton industry? Question: What has the Cotton-4 been in regards to the US cotton subsidies? Question: What could the US do that would please the foreign cotton industry concerning cotton subsidies?
In addition to concerns over subsidies, the cotton industries of some countries are criticized for employing child labor and damaging workers' health by exposure to pesticides used in production. The Environmental Justice Foundation has campaigned against the prevalent use of forced child and adult labor in cotton production in Uzbekistan, the world's third largest cotton exporter. The international production and trade situation has led to "fair trade" cotton clothing and footwear, joining a rapidly growing market for organic clothing, fair fashion or "ethical fashion". The fair trade system was initiated in 2005 with producers from Cameroon, Mali and Senegal.

Question: What type of labor have some countries come under fire for employing? Question: What usage that causes worker damage have some countries been reported doing? Question: What country has been accused of forced child and adult labor? Question: Where does Uzbekistan rank as a cotton exporter? Question: What system was started in 2005 to deal with organic and ethically produced products?
A public genome sequencing effort of cotton was initiated in 2007 by a consortium of public researchers. They agreed on a strategy to sequence the genome of cultivated, tetraploid cotton. "Tetraploid" means that cultivated cotton actually has two separate genomes within its nucleus, referred to as the A and D genomes. The sequencing consortium first agreed to sequence the D-genome relative of cultivated cotton (G. raimondii, a wild Central American cotton species) because of its small size and limited number of repetitive elements. It is nearly one-third the number of bases of tetraploid cotton (AD), and each chromosome is only present once.[clarification needed] The A genome of G. arboreum would be sequenced next. Its genome is roughly twice the size of G. raimondii's. Part of the difference in size between the two genomes is the amplification of retrotransposons (GORGE). Once both diploid genomes are assembled, then research could begin sequencing the actual genomes of cultivated cotton varieties. This strategy is out of necessity; if one were to sequence the tetraploid genome without model diploid genomes, the euchromatic DNA sequences of the AD genomes would co-assemble and the repetitive elements of AD genomes would assembly independently into A and D sequences respectively. Then there would be no way to untangle the mess of AD sequences without comparing them to their diploid counterparts.

Question: When did a group of researchers decide to sequence the genomic structure of tetraploid cotton? Question: What is the final sequencing goal of sequencing diploid cotton genomes first ? Question: What type of cotton has two separate genomes within its nucleus? Question: What type of genome must be sequenced first to prevent confusion before the tetraploid form? Question: In order to understand the tetraploid forms, what must be used as a comparison in cotton gene sequencing?
The public sector effort continues with the goal to create a high-quality, draft genome sequence from reads generated by all sources. The public-sector effort has generated Sanger reads of BACs, fosmids, and plasmids as well as 454 reads. These later types of reads will be instrumental in assembling an initial draft of the D genome. In 2010, two companies (Monsanto and Illumina), completed enough Illumina sequencing to cover the D genome of G. raimondii about 50x. They announced that they would donate their raw reads to the public. This public relations effort gave them some recognition for sequencing the cotton genome. Once the D genome is assembled from all of this raw material, it will undoubtedly assist in the assembly of the AD genomes of cultivated varieties of cotton, but a lot of hard work remains.

Question: When was the sequencing of D genome of G.  raimondii mostly completed? Question: What companies finished much of the sequencing of the D genome? Question: What did Monsanto and Illumina say they would do with the research? Question: What is the seeming result of donating genetic cotton sequencing to the public? Question: What plants are the goal of sequencing wild forms of cotton?
In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation. Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying unnecessary information and removing it. The process of reducing the size of a data file is referred to as data compression. In the context of data transmission, it is called source coding (encoding done at the source of the data before it is stored or transmitted) in opposition to channel coding.

Question: What involves encoding information using fewer bits than the original representation? Question: What can be either lossy or lossless? Question: What reduces bits by identifying and eliminating statistical redundancy? Question: What is the process called of reducing the size of a data file? Question: What is the process called of encoding at the source of the data before it's processed?
Compression is useful because it helps reduce resource usage, such as data storage space or transmission capacity. Because compressed data must be decompressed to use, this extra processing imposes computational or other costs through decompression; this situation is far from being a free lunch. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data.

Question: What helps reduce resource usage? Question: What must be decompressed? Question: What is subject to a space-time complexity trade off? Question: What type of scheme involves trade offs among other things?
Lossless data compression algorithms usually exploit statistical redundancy to represent data without losing any information, so that the process is reversible. Lossless compression is possible because most real-world data exhibits statistical redundancy. For example, an image may have areas of colour that do not change over several pixels; instead of coding "red pixel, red pixel, ..." the data may be encoded as "279 red pixels". This is a basic example of run-length encoding; there are many schemes to reduce file size by eliminating redundancy.

Question: What usually exploits statistical redundancy? Question: What represents statistical redundancy? Question: What may have colors that do not change over several pixels?
The Lempel–Ziv (LZ) compression methods are among the most popular algorithms for lossless storage. DEFLATE is a variation on LZ optimized for decompression speed and compression ratio, but compression can be slow. DEFLATE is used in PKZIP, Gzip and PNG. LZW (Lempel–Ziv–Welch) is used in GIF images. Also noteworthy is the LZR (Lempel-Ziv–Renau) algorithm, which serves as the basis for the Zip method.[citation needed] LZ methods use a table-based compression model where table entries are substituted for repeated strings of data. For most LZ methods, this table is generated dynamically from earlier data in the input. The table itself is often Huffman encoded (e.g. SHRI, LZX). Current LZ-based coding schemes that perform well are Brotli and LZX. LZX is used in Microsoft's CAB format.

Question: What compression methods are among the most popular? Question: What is used in PKZIP, Gzip and PNG? Question: What serves as the basis of the Zip method? Question: What is a current LZ based coding scheme that does well? Question: What is used in Microsoft's CAB format?
In a further refinement of the direct use of probabilistic modelling, statistical estimates can be coupled to an algorithm called arithmetic coding. Arithmetic coding is a more modern coding technique that uses the mathematical calculations of a finite-state machine to produce a string of encoded bits from a series of input data symbols. It can achieve superior compression to other techniques such as the better-known Huffman algorithm. It uses an internal memory state to avoid the need to perform a one-to-one mapping of individual input symbols to distinct representations that use an integer number of bits, and it clears out the internal memory only after encoding the entire string of data symbols. Arithmetic coding applies especially well to adaptive data compression tasks where the statistics vary and are context-dependent, as it can be easily coupled with an adaptive model of the probability distribution of the input data. An early example of the use of arithmetic coding was its use as an optional (but not widely used) feature of the JPEG image coding standard. It has since been applied in various other designs including H.264/MPEG-4 AVC and HEVC for video coding.

Question: What can be paired with an algorithm called arithmetic coding? Question: What can achieve superior compression? Question: What uses an internal memory state?
Lossy data compression is the converse of lossless data compression. In these schemes, some loss of information is acceptable. Dropping nonessential detail from the data source can save storage space. Lossy data compression schemes are designed by research on how people perceive the data in question. For example, the human eye is more sensitive to subtle variations in luminance than it is to the variations in color. JPEG image compression works in part by rounding off nonessential bits of information. There is a corresponding trade-off between preserving information and reducing size. A number of popular compression formats exploit these perceptual differences, including those used in music files, images, and video.

Question: What type of data compression is the converse of lossless date data compression? Question: What can save storage space? Question: What helps by eliminating off nonessential bits of information?
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.

Question: What methods are used to remove non-audible components of audio signals? Question: What compression is usually performed with even more specialized techniques? Question: What is used in internet telephony? Question: What is used for CD ripping? Question: What is encoded by audio players?
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."

Question: What is Machine learning closely related to? Question: What type of compressor can be used for prediction? Question: What has been used as a benchmark for "general intelligence"?
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.

Question: What can be looked at as a special case of data differencing? Question: What is made up of producing a difference given a source and a target? Question: What can classified as data differencing with empty source data?
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.

Question: What has the potential to reduce the transmission bandwidth and storage requirements of audio data? Question: What is inserted in software as audio codecs? Question: What compression algorithms provide higher compression at the cost of fidelity?
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.

Question: What produces a representation of digital data that decompresses? Question: What are around 50-60% of the original size? Question: What compression cannot attain high 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.

Question: What compression is used in a lot of applications? Question: What is used in most video DVDs? Question: What compression usually achieves far greater compression then lossless?
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.

Question: What is the process called when a signal is masked by another signal separated by time? Question: What may be used to weight the importance of components? Question: What are often called psychoacoustic models?
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.

Question: What is the coding called that is used with speech? Question: What is thought of as a basic perceptual coding technique? Question: What uses a model of the sound's generator?
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.

Question: What results from methods used to encode and decode data? Question: What analyzes a statement of data to boost performance? Question: What creates segments called a "frame"?
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)).

Question: What refers to the number of samples that must be analysed before a block of audio is processed? Question: What has zero samples in a minimum case? Question: What often has low latencies?
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.

Question: What has to be compressed in order to perform properly? Question: What varies with time? Question: What does "A/D" stand for?