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U.S. National Geodetic Survey

The National Geodetic Survey (NGS) is a United States federal agency based in Washington, D.C. that defines and manages a national coordinate system, providing the foundation for transportation and communication, mapping and charting, and a large number of science and engineering applications. Since its founding in 1970, it has been part of the National Oceanic and Atmospheric Administration (NOAA), a division within the United States Department of Commerce.

History
The National Geodetic Survey's history and heritage are intertwined with those of other NOAA offices. It traces its history to the Survey of the Coast, which was formed in 1807 as the first scientific agency of the United States federal government. It became the United States Coast Survey in 1836 and the United States Coast and Geodetic Survey in 1878, the latter name change reflecting the increasing role of geodesy in its work. Upon the creation of NOAA in 1970, the Coast and Geodetic Survey was abolished and its responsibilities were split among various agencies and offices of NOAA. The Coast and Geodetic Survey′s former geodetic responsibilities were placed under the new National Geodetic Survey in NOAA's National Ocean Survey (later renamed the National Ocean Service).

In 2009, former National Oceanic and Atmospheric Administration Commissioned Officer Corps officer Juliana P. Blackwell was named Director of the National Geodetic Survey, becoming the first woman to head either NGS or any of its ancestor organizations.

Purpose and function
The National Geodetic Survey is an office of NOAA's National Ocean Service. Its core function is to maintain the National Spatial Reference System (NSRS), "a consistent coordinate system that defines latitude, longitude, height, scale, gravity, and orientation throughout the United States". NGS is responsible for defining the NSRS and its relationship with the International Terrestrial Reference Frame (ITRF). The NSRS enables precise and accessible knowledge of where things are in the United States and its territories.

The NSRS may be divided into its geometric and physical components. The official geodetic datum of the United States, NAD83 defines the geometric relationship between points within the United States in three-dimensional space. The datum may be accessed via NGS's network of survey marks or through the Continuously Operating Reference Station (CORS) network of GPS reference antennas. NGS is responsible for computing the relationship between NAD83 and the ITRF. The physical components of the NSRS are reflected in its height system, defined by the vertical datum NAVD88. This datum is a network of orthometric heights obtained through spirit leveling. Because of the close relationship between height and Earth's gravity field, NGS also collects and curates terrestrial gravity measurements and develops regional models of the geoid (the level surface that best approximates sea level) and its slope, the deflection of the vertical. NGS is responsible for ensuring the accuracy of the NSRS over time, even as the North American plate rotates and deforms over time due to crustal strain, post-glacial rebound, subsidence, elastic deformation of the crust, and other geophysical phenomena.

NGS will release new datums in 2022. The North American Terrestrial Reference Frame of 2022 (NATRF2022) will supersede NAD83 in defining the geometric relationship between the North American plate and the ITRF. United States territories on the Pacific, Caribbean, and Mariana plates will have their own respective geodetic datums. The North American-Pacific Geopotential Datum of 2022 (NAPGD2022) will separately define the height system of the United States and its territories, replacing NAVD88. It will use a geoid model accurate to 1 centimeter (0.4") to relate orthometric height to ellipsoidal height measured by GPS, eliminating the need for future leveling projects. This geoid model will be based on airborne and terrestrial gravity measurements collected by NGS's GRAV-D program as well as satellite-based gravity models derived from observations collected by GRACE, GOCE, and satellite altimetry missions.

NGS provides a number of other public services. It maps changing shorelines in the United States and provides aerial imagery of regions affected by natural disasters, enabling rapid damage assessment by emergency managers and members of the public. The Online Positioning and User Service (OPUS) processes user-input GPS data and outputs position solutions within the NSRS. The agency offers other tools for conversion between datums.

title: U.S. National Geodetic Survey


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NSBM

NSBM may refer to:

 * National School of Business Management, a Sri Lankan tertiary institute
 * National Socialist black metal, a political philosophy within black metal music that promotes Nazism or similar ideologies
 * Nederlandse Stoomboot Maatschappij, a Dutch shipping line later absorbed into Nedlloyd as the Koninklijk Nederlandse Stoomboot Maatschappij (KNSM)
 * No sex before marriage
 * Northeast School of Botanical Medicine, located in Ithaca, New York

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: NSBM


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Polidoro da Caravaggio

Polidoro Caldara, usually known as Polidoro da Caravaggio (c. 1499 – 1543) was an Italian painter of the Mannerist period, "arguably the most gifted and certainly the least conventional of Raphael's pupils", who was best known for his now-vanished paintings on the facades of Roman houses. He was unrelated to the later painter Michelangelo Merisi da Caravaggio, usually known just as Caravaggio, but both came from the town of Caravaggio.

Life and work

Polidoro Caldara was born in Caravaggio, in what is now Lombardy. According to Vasari, whilst working as a labourer carrying the materials for the builders of the Vatican logge he ingratiated himself with the artists, and attracted the admiration of Maturino da Firenze, one of Raphael's main assistants in the ongoing decoration of the Vatican. He then joined Raphael's large workshop, in about 1517, and worked on the Raphael Rooms in the Vatican. He and Maturino then set up as painters of palace facades, usually in sgraffito, with considerable success until the sack of Rome by the army of Charles V under the command of Constable de Bourbon in 1527, in which Maturino was killed. Polidoro fled to Naples, and from there to Messina, where he was very successful. According to tradition, he was about to return from there to the mainland of Italy when he was robbed and murdered by an assistant, Tonno Calabrese, in 1543.

In the second half of the sixteenth century his tomb in Messina (inside Carmine Maggiore, with the tomb of Constantine Lascaris) was totally destroyed during the repression of the Counter-Reformation.

Polidoro's main paintings include a Crucifixion, painted in Messina, and a Deposition of Christ (1527) and a Christ Carrying the Cross (1530–34) both in the Museo di Capodimonte of Naples, who have the best collection of his work  (an oil sketch for the latter is in the National Gallery, London). They are very individual in style, extremely free in technique, and powerful in expression. The Christ bearing the Cross shows considerable Northern influence, probably reflecting the traditionally strong links between Sicily and the Netherlands.

His other works, as well as those of his partner, Maturino da Firenze, have mostly perished from exposure, as most were external decorations on the facades of palaces, but are known from many etchings by Pietro Santi Bartoli, Cherubino Alberti and others. One of his pupils is Deodato Guinaccia. They were authors of the facade decoration in classicising Graffito, usually in grisaille, of several Roman houses, like those ones in Borgo and in Parione (near Santa Maria della Pace and in Via del Pellegrino). A series of nine small internal wood panels from an unknown palace, perhaps in Naples, of which eight are now in the English Royal Collection, and one in the Louvre, give an idea of the liveliness and quality of these lost works: "Polidoro learned from Raphael the idea of re-creating the decoration of classical antiquity; but he did so with a wit, freedom and spirit of his own". Being always visible to the public, whilst they lasted the palace facades were very well known and influential, and used by "generations of young artists ... as a visual textbook". There are also many surviving drawings of high quality.

Assassination
According to Vasari, Polidoro was firmly resolved to return to Rome after completing significant projects in Messina. In order to make preparations for this trip, he withdrew all of his savings from the bank for the trip to Rome. Upon discovering this, one of Polidoro's workmen, along with several accomplices, resolved to put the master to death on the following night, and then to divide the money among themselves. On the night following, they set upon Polidoro while he was slumbering deeply, and strangled him with a cloth. Then, giving him several wounds, they made sure of his death; After a period of many days when no perpetrator was discovered, it was thought that no one except the workman could have committed the act. Upon receiving intelligence of the assistant's alleged involvement, he was captured on the authority of the Count of Messina, and tortured until he confessed to the crime. Shortly thereafter, he was sentenced to the gallows, torn with red-hot pincers, and quartered.

Legacy
Among Polidoro's pupils in Messina was Deodato Guinaccia, Stefano Giordano, Mariano Riccio, Antonello Riccio, Jacopo Vignerio, and Alfonso Lazzaro.

title: Polidoro da Caravaggio


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A Personal Record

A Personal Record is an autobiographical work (or "fragment of biography") by Joseph Conrad, published in 1912.

It has also been published under the titles A Personal Record: Some Reminiscences and Some Reminiscences.

Notoriously unreliable and digressive in structure, it is nonetheless the principal contemporary source for information about the author's life. It tells about his schooling in Russian Poland, his sailing in Marseille, the influence of his uncle Tadeusz Bobrowski, and the writing of Almayer's Folly.

It provides a glimpse of how Conrad wished to be seen by his British public, as well as being an atmospheric work of art.

The "Familiar Preface" Conrad wrote for it includes the often quoted lines:

"Those who read me know my conviction that the world, the temporal world, rests on a few very simple ideas; so simple that they must be as old as the hills. It rests notably, among others, on the idea of Fidelity."

Conrad wrote a new 'Author's Note' to A Personal Record for the Doubleday collected edition of his works (published in 1920) in which he discussed his friendship with the British colonial official and writer Hugh Clifford.

title: A Personal Record


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Bessin

Bessin is an area in Normandy, France, corresponding to the territory of the Bajocasses, a Gallic tribe from whom Bayeux, its main town, takes its name.

History
The territory was annexed by the count of Rouen in 924.

The Bessin corresponds to the former diocese of Bayeux, which was incorporated into the Calvados département following the French Revolution.

Ecology
Part of the Bessin is now administered as a national park for its importance as marshland.

title: Bessin


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Knjaz Varggoth

Yevhen Hapon (Євген Гапон), also spelled Eugeny Gapon and Ievgen Gapon, better known by his stage name Knjaz Varggoth, is one of the foremost figures in Eastern European black metal and far-right musicians. He is also known to Russians as Yevgeny Gaponovich Gaponovskiy (Евгений Гапонович Гапоновский) or Genka for short.

Biography
Yevhen Hapon was born 30 July 1974. He is better known by his stage name Knjaz Varggoth.

Knjaz Varggoth is one of the foremost figures in Eastern Europe black metal and far-right musicians. Though he has been involved with numerous music projects and bands (Aryan Terrorism, Mistigo Varggoth Darkestra, Lucifugum as a guest musician in 1997, Вече, and more), he is most well known for being the lead vocalist and guitarist for the controversial Ukrainian black metal band Nokturnal Mortum. Nokturnal Mortum gained their reputation performing symphonic black metal with elements of Ukrainian and Slavic folk music and for being a part of the eastern European NSBM scene.

A side project done by Varggoth is the ambient/black metal project Mistigo Varggoth Darkestra, formed in 1994. The most famous album of this project, The Key to the Gates of the Apocalypse, is most likely known particularly due to its long runtime (one 72-minute track) and combination of dark ambient and fast black metal.

Knjaz Varggoth also formed the Neo-Nazi hatecore group Aryan Terrorism.

Suppuration

 * 1992: Ecclesiastical Blasphemy

Crystaline Darkness

 * 1993: Mi Agama Khaz Mifisto

Mistigo Varggoth Darkestra

 * 1997: Midnight Fullmoon
 * 1999: The Key to the Gates of the Apocalypse

Nokturnal Mortum

 * 1995: Twilightfall
 * 1996: Lunar Poetry
 * 1997: Goat Horns
 * 1998: To the Gates of Blasphemous Fire
 * 1999: Nechrist
 * 2005: Weltanschauung
 * 2009: The Voice of Steel
 * 2017: Verity

Aryan Terrorism

 * 2002: W.A.R

Vetche

 * 1998: Vetche

Piorun

 * 2004: Stajemy Jak Ojce

Temnozor
Live member

title: Knjaz Varggoth


text:
Federico Barocci

Federico Barocci (also written Barozzi) (c. 1535 – 30 September 1612) was an Italian Renaissance painter and printmaker. His original name was Federico Fiori, and he was nicknamed Il Baroccio. His work was highly esteemed and influential, and foreshadows the Baroque of Rubens. He is generally considered the greatest and the most individual painter of his time in central Italy.

Early life and training

He was born at Urbino, Duchy of Urbino, and received his earliest apprenticeship with his father, Ambrogio Barocci, a sculptor of some local eminence. He was then apprenticed with the painter Battista Franco Veneziano in Urbino. He accompanied his uncle, Bartolommeo Genga to Pesaro, then in 1548 to Rome, where he was worked in the pre-eminent studio of the day, that of the Mannerist painters, Taddeo and Federico Zuccari.

Mature work in Rome and Urbino
After passing four years at Rome, he returned to his native city, where his first work of art was a St. Margaret executed for the Confraternity of the Holy Sacrament. He was invited back to Rome by Pope Pius IV to assist in the decoration of the Vatican Belvedere Palace at Rome, where he painted the Virgin Mary and infant, with several Saints and a ceiling in fresco, representing the Annunciation.

During this second sojourn, while completing the decorations for the Vatican, Barocci fell ill with intestinal complaints. He suspected that a salad which he had eaten had been poisoned by jealous rivals. Fearing his illness was terminal, he left Rome in 1563; four years later he was said to experience a partial remission after prayers to the Virgin. Barocci henceforth often complained of frail health, though he remained productive for nearly four decades more. While he is described by contemporaries as personally somewhat morose and hypochondriacal, his paintings are lively and brilliant. Although he continued to have major altarpiece commissions from afar, he never returned to Rome, and was mainly patronized in his native city by Francesco Maria II della Rovere, duke of Urbino. The Ducal Palace can be seen in the background of his paintings, rendered in a forced perspective that seems a holdover from Mannerism.

While Barocci was removed from Rome, the fulcrum of artistic fame and influence, he continued to innovate in his style. At some point he may have seen colored chalk/pastel drawings by Correggio, but Barocci's remarkable pastel studies are the earliest examples of the technique to survive. In pastels and in oil sketches (another technique he pioneered) Barocci's soft, opalescent renderings evoke the ethereal. Such studies were part of a complex process Barocci used to complete his altarpieces. An organized series of steps leading up to the final product ensured its speed and success in execution. Barocci did innumerable sketches: gestural, compositional, figural studies (using models), lighting studies (using clay models), perspective studies, color studies, nature studies, etc. Today, over 2,000 drawings by him are extant. Every detail of his subsequent cartoons for canvases was worked out in this way. A good example is his famed Madonna del Popolo (Uffizi). It is a vortex of color and vitality, made possible by the great variety of people, poses, perspectives, natural details, colors, lighting and atmospheric effects. There are many surviving drawings for the Madonna del Popolo, from initial sketches to color studies of heads, to the final full size cartoon. Despite this painstaking process, Barocci's genius kept the brushstrokes passionate and liberated, and a spiritual light seems to flicker as a jewel across faces, hands, drapery, and sky.

Barocci's embrace of the Counter Reformation would shape his long and fruitful career. By 1566, he joined a lay order of Capuchins, an offshoot of Franciscans. He may have been influenced by Saint Philip Neri, whose Oratorians sought to reconnect the spiritual realm with the lives of everyday people. Neri, who was somewhat ambivalent about the accumulating richness of his Santa Maria in Vallicella, commissioned two completed works from Barocci, the pre-eminent artist of these large pious altarpieces: The Visitation (1583–6) and Presentation of the Virgin (1593–94). Neri is said to have been moved to ecstasy by Barocci's accomplishment in the former painting, which shows the Virgin and Elizabeth greeting each other.

In Urbino, where he painted a Descent from the Cross for the cathedral of San Lorenzo at Perugia. He again visited Rome during the papacy of Gregory XIII when he painted two admirable pictures for the Chiesa Nuova, representing the Visitation of the Virgin Mary to Elisabeth and the  Presentation in the Temple, and for the Chiesa della Minerva, a Last Supper.

Critical assessment and legacy
The artist biographer Giovanni Bellori, the Baroque equivalent of Giorgio Vasari, considered Barocci to be among the finest painters of his time. Barocci's emotive brushwork was not lost on Peter Paul Rubens when he was in Italy. Rubens is known to have made a sketch of his dramatic Martyrdom of St Vitale, in which the martyr's undulating flesh is the eye of another whirlwind of figures, gestures, and drama. Also, Rubens' The Martyrdom of St Livinus seems to owe much to Barocci, from the putto with the pointing palm frond to the presence of dogs in the lower right corner. Among the painters and artists who worked under Barocci are Antonio Cimatori (Visacci), Ventura Mazza, Antonio Viviani (il Sordo di Urbino), Giovanni Andrea Urbani, Alessandro Vitali, and finally Felice and Vincenzo Pellegrini. Barocci also had many who followed or were strongly influenced by his style, including Nicolo Martinelli (il Trometta), Giovanni Battista Lombardelli, Domenico Malpiedi, Cesare & Basilio Maggeri, Filippo Bellini, Giovanni Laurentini (Arrigoni), Giorgio Picchi, Giovanni Giacomo Pandolfi, Pietro Paolo Tamburini, Terenzio d’Urbino (il Rondolino), Giulio Cesare Begni, Benedetto Marini, Girolamo Cialdieri, Giovanni Battista Urbinelli, Alfonso Patanazzi, Gian Ortensio Bertuzzi, Cesare Franchi (il Pollino), Silla Piccinini, Benedetto Bandiera, Matteuccio Salvucci, Simeone Ciburri, Pietro Rancanelli, Onofrio Marini, Alessandro Brunelli, and Francesco Baldelli.

Barocci's swirling composition and the focus on the emotional and spiritual are elements that foreshadow the Baroque of Rubens. But even in Federico's Proto-Baroque Beata Michelina can see the makings of Bernini's High Baroque masterpiece Ecstasy of St Theresa.

title: Federico Barocci


text: Category:Living Colour albums

title: Category:Living Colour albums


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Grazing

In agriculture, grazing is a method of animal husbandry whereby domestic livestock are allowed outdoors to free range (roam around) and consume wild vegetations in order to convert the otherwise indigestible (by human gut) cellulose within grass and other forages into meat, milk, wool and other animal products, often on land that is unsuitable for arable farming.

Farmers may employ many different strategies of grazing for optimum production: grazing may be continuous, seasonal, or rotational within a grazing period. Longer rotations are found in ley farming, alternating arable and fodder crops; in rest rotation, deferred rotation, and mob grazing, giving grasses a longer time to recover or leaving land fallow. Patch-burn sets up a rotation of fresh grass after burning with two years of rest. Conservation grazing proposes to use grazing animals to improve the biodiversity of a site.

Grazing has existed since the beginning of agriculture; sheep and goats were domesticated by nomads before the first permanent settlements were constructed around 7000 BC, enabling cattle and pigs to be kept.

Livestock grazing contributes to many negative effects on the environment, including deforestation, extinction of native wildlife, pollution of streams and rivers, overgrazing, soil degradation, ecological disturbance, desertification, and ecosystem stability.

History
Sheep, goats, cattle, and pigs were domesticated early in the history of agriculture. Sheep were domesticated first, soon followed by goats; both species were suitable for nomadic peoples. Cattle and pigs were domesticated somewhat later, around 7000 BC, once people started to live in fixed settlements.

In America, livestock were grazed on public land from the Civil War. The Taylor Grazing Act of 1934 was enacted after the Great Depression to regulate the use of public land for grazing purposes.

Production
According to a report by the Food and Agriculture Organization, about 60% of the world's grassland (just less than half of the world's usable surface) is covered by grazing systems. It states that "Grazing systems supply about 9 percent of the world's production of beef and about 30 percent of the world's production of sheep and goat meat. For an estimated 100 million people in arid areas, and probably a similar number in other zones, grazing livestock is the only possible source of livelihood."

Management

Grazing management has two overall goals:

 * 1) Protecting the quality of the pasturage against deterioration by overgrazing: in other words, maintain the sustainability of the pasturage
 * 2) Protecting the health of the animals against acute threats, such as:
 * 3) * Grass tetany and nitrate poisoning
 * 4) * Trace element overdose, such as molybdenum and selenium poisoning
 * 5) * Grass sickness and laminitis in horses
 * 6) * Milk sickness in calves

A proper land use and grazing management technique balances
 * maintenance of forage and livestock production, with
 * maintenance of biodiversity and ecosystem services.

It does this by allowing sufficient recovery periods for regrowth. Producers can keep a low density on a pasture, so as not to overgraze. Controlled burning of the land can help in the regrowth of plants. Although grazing can be problematic for the ecosystem, well-managed grazing techniques can reverse damage and improve the land.

On commons in England and Wales, rights of pasture (grassland grazing) and pannage (forest grazing) for each commoner are tightly defined by number and type of animal, and by the time of year when certain rights can be exercised. For example, the occupier of a particular cottage might be allowed to graze fifteen cattle, four horses, ponies or donkeys, and fifty geese, while the numbers allowed for their neighbours would probably be different. On some commons (such as the New Forest and adjoining commons), the rights are not limited by numbers, and instead a 'marking fee' is paid each year for each animal 'turned out'. However, if excessive use was made of the common, for example, in overgrazing, a common would be 'stinted'; that is, a limit would be put on the number of animals each commoner was allowed to graze. These regulations were responsive to demographic and economic pressure. Thus, rather than let a common become degraded, access was restricted even further.

Systems
Ranchers and range science researchers have developed grazing systems to improve sustainable forage production for livestock. These can be contrasted with intensive animal farming on feedlots.

Continuous

With continuous grazing, livestock is allowed access to the same grazing area throughout the year.

Seasonal
Seasonal grazing incorporates "grazing animals on a particular area for only part of the year". This allows the land that is not being grazed to rest and allow for new forage to grow.

Rotational

Rotational grazing "involves dividing the range into several pastures and then grazing each in sequence throughout the grazing period". Utilizing rotational grazing can improve livestock distribution while incorporating rest period for new forage.

Ley farming
In ley farming, pastures are not permanently planted, but alternated between fodder crops and arable crops.

Rest rotation
Rest rotation grazing "divides the range into at least four pastures. One pasture remains rested throughout the year and grazing is rotated amongst the residual pastures." This grazing system can be especially beneficial when using sensitive grass that requires time for rest and regrowth.

Deferred rotation
Deferred rotation "involves at least two pastures with one not grazed until after seed-set". By using deferred rotation, grasses can achieve maximum growth during the period when no grazing occurs.

Patch-burn
Patch-burn grazing burns a third of a pasture each year, no matter the size of the pasture. This burned patch attracts grazers (cattle or bison) that graze the area heavily because of the fresh grasses that grow as a result. The other patches receive little to no grazing. During the next two years the next two patches are burned consecutively, then the cycle begins anew. In this way, patches receive two years of rest and recovery from the heavy grazing. This technique results in a diversity of habitats that different prairie plants and birds can utilize—mimicking the effects of the pre-historical relationship between bison and fire, whereby bison heavily graze one area and other areas have opportunity to rest, based on the concept of pyric herbivory. The Tallgrass Prairie Preserve in northeastern Oklahoma has been patch-burn grazed with bison herds for over ten years. These efforts have effectively restored the bison–fire relationship on a large landscape scale of 30000 acre. In the grazed heathland of Devon, the periodic burning is known as swailing.

Riparian area management
Riparian area grazing is intended to improve wildlife and their habitats. It uses fencing to keep livestock off ranges near streams or water areas until after wildlife or waterfowl periods, or to limit the amount of grazing to a short period of time.

Conservation grazing

Conservation grazing is the use of grazing animals to help improve the biodiversity of a site. Due to their hardy nature, rare and native breeds are often used in conservation grazing. In some cases, to re-establish traditional hay meadows, cattle such as the English Longhorn and Highland are used to provide grazing.

Cell grazing
A form of rotational grazing using as many small paddocks as fencing allows, said to be more sustainable.

Mob grazing
Mob grazing is a system, said to be more sustainable, invented in 2002; it uses very large herds on land left fallow longer than usual.

Ecology

A number of ecological effects derive from grazing, and these may be either positive or negative. Negative effects of grazing may include overgrazing, increased soil erosion, compaction and degradation, deforestation, biodiversity loss, and adverse water quality impacts from run-off. Sometimes grazers can have beneficial environmental effects such as improving the soil with nutrient redistribution and aerating the soil by trampling, and by controlling fire and increasing biodiversity by removing biomass, controlling shrub growth and dispersing seeds. In some habitats, appropriate levels of grazing may be effective in restoring or maintaining native grass and herb diversity in rangeland that has been disturbed by overgrazing, lack of grazing (such as by the removal of wild grazing animals), or by other human disturbance. Conservation grazing is the use of grazers to manage such habitats, often to replicate the ecological effects of the wild relatives of domestic livestock, or those of other species now absent or extinct.

Grazer urine and faeces "recycle nitrogen, phosphorus, potassium and other plant nutrients and return them to the soil". Grazing can reduce the accumulation of litter (organic matter) in some seasons and areas, but can also increase it, which may help to combat soil erosion. This acts as nutrition for insects and organisms found within the soil. These organisms "aid in carbon sequestration and water filtration".

When grass is grazed, dead grass and litter are reduced which is advantageous for birds such as waterfowl. Grazing can increase biodiversity. Without grazing, many of the same grasses grow, for example brome and bluegrass, consequently producing a monoculture. The ecosystems of North American tallgrass prairies are controlled to a large extent by nitrogen availability, which is itself controlled by interactions between fires and grazing by large herbivores. Fires in spring enhance growth of certain grasses, and herbivores preferentially graze these grasses, producing a system of checks and balances, and allowing higher plant biodiversity. In Europe heathland is a cultural landscape which requires grazing by cattle, sheep or other grazers to be maintained.

Conservation
An author of the Food and Agriculture Organization (FAO) report Livestock's Long Shadow, stated in an interview:

"Grazing occupies 26 percent of Earth's terrestrial surface... feed crop production requires about a third of all arable land... Expansion of grazing land for livestock is also a leading cause of deforestation, especially in Latin America... In the Amazon basin alone, about 70 percent of previously forested land is used as pasture, while feed crops cover a large part of the remainder."

Much grazing land has resulted from a process of clearance or drainage of other habitats such as woodland or wetland.

According to the opinion of the Center for Biological Diversity, extensive grazing of livestock in the arid lands of the southwestern United States has many negative impacts on the local biodiversity there. "Cattle destroy native vegetation, damage soils and stream banks, and contaminate waterways with fecal waste. After decades of livestock grazing, once-lush streams and riparian forests have been reduced to flat, dry wastelands; once-rich topsoil has been turned to dust, causing soil erosion, stream sedimentation and wholesale elimination of some aquatic habitats"

In arid climates such as the southwestern United States, livestock grazing has severely degraded riparian areas, the wetland environment adjacent to rivers or streams. The Environmental Protection Agency states that agriculture has a greater impact on stream and river contamination than any other nonpoint source. Improper grazing of riparian areas can contribute to nonpoint source pollution of riparian areas. Riparian zones in arid and semiarid environments have been called biodiversity hotspots. The water, higher biomass, favorable microclimate and periodic flood events together produce higher biological diversity than in the surrounding uplands. In 1990, "according to the Arizona state park department, over 90% of the original riparian zones of Arizona and New Mexico are gone". A 1988 report of the Government Accountability Office estimated that 90% of the 5,300 miles of riparian habitat managed by the Bureau of Land Management in Colorado was in unsatisfactory condition, as was 80% of Idaho's riparian zones, concluding that "poorly managed livestock grazing is the major cause of degraded riparian habitat on federal rangelands".

A 2013 FAO report estimated livestock were responsible for 14.5% of anthropogenic greenhouse gas emissions. Grazing is common in New Zealand; in 2004, methane and nitrous oxide from agriculture made up somewhat less than half of New Zealand's greenhouse gas emissions, of which most is attributable to livestock. A 2008 United States Environmental Protection Agency report on emissions found agriculture was responsible for 6% of total United States greenhouse gas emissions in 2006. This included rice production, enteric fermentation in domestic livestock, livestock manure management, and agricultural soil management, but omitted some things which might be attributable to agriculture. Studies comparing the methane emissions from grazing and feedlot cattle concluded that grass-fed cattle produce much more methane than grain-fed cattle. One study in the Journal of Animal Science found four times as much, and stated: "these measurements clearly document higher CH4 production for cattle receiving low-quality, high-fiber diets than for cattle fed high-grain diets".

Agrivoltaics
Agrivoltaics for grazing would allow for shade for the animals as well as the vegitation so the soil retains a higher moisture level.

title: Grazing


text:
Talk:Vivid (Living Colour album)

Proposed page move
For the sake of simplicity and clarity, I would like to rename this article to Vivid (album) and move the disambig page from Vivid (disambiguation) to Vivid. I feel this would be more consistent and prevent mistaken links. If there are objections to this, I would like to discuss them. Thank you. -EdgarAllanToe 12:51, 19 September 2005 (UTC)

Fair use rationale for Image:Living Colour-Vivid.jpg
Image:Living Colour-Vivid.jpg is being used on this article. I notice the image page specifies that the image is being used under fair use but there is no explanation or rationale as to why its use in this Wikipedia article constitutes fair use. In addition to the boilerplate fair use template, you must also write out on the image description page a specific explanation or rationale for why using this image in each article is consistent with fair use.

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If there is other fair use media, consider checking that you have specified the fair use rationale on the other images used on this page. Note that any fair use images lacking such an explanation can be deleted one week after being tagged, as described on criteria for speedy deletion. If you have any questions please ask them at the Media copyright questions page. Thank you.

BetacommandBot (talk) 23:39, 13 February 2008 (UTC)

title: Talk:Vivid (Living Colour album)


text:
Wilhelm Paul Corssen

Wilhelm Paul Corssen (20 January 1820 – 18 June 1875) was a German philologist noted for his work on Latin and Etruscan topics.

Biography
Corssen was born in Bremen and, after his merchant father moved to Schwedt in the Kingdom of Prussia, received his school education there. After spending some time at the Joachimsthal-Gymnasium in Berlin, where his interest in philological pursuits was awakened by the rector, Meinike, he proceeded to the university, and there came especially under the influence of Philipp August Böckh and Karl Lachmann.

His first important appearance in literature was as the author of Origines poesis romanae, by which he had obtained the prize offered by the philosophical or arts faculty of the university. In 1846 he was called from Stettin, where he had for nearly two years held a post in the gymnasium, to occupy the position of lecturer in the royal academy at Pforta (commonly called Schulpforta), and there he continued to labour for the next twenty years.

In 1854 he won a prize offered by the Royal Prussian Academy of Sciences for the best work on the pronunciation and accent of Latin, a treatise which at once took rank, on its publication under the title of Über Aussprache, Vocalismus, und Betonung der lateinischen Sprache (1858–1859), as one of the most erudite and masterly works in its department. This was followed in 1863 by his Kritische Beiträge zur lat. Formenlehre, which were supplemented in 1866 by Kritische Nachträge zur lat. Formenlehre.

In the discussion of the pronunciation of Latin he was naturally led to consider the various old Italian dialects, and the results of his investigations appeared in miscellaneous communications to Franz Felix Adalbert Kuhn's Zeitschrift für vergleichende Schriftforschung. Ill-health obliged him to give up his professorship at Pforta and return to Berlin in 1866, but it produced almost no diminution of his literary activity. In 1867 he published an elaborate archaeological study entitled the Alterthümer und Kunstdenkmale des Gistercienserklosters St Marien und der Landesschule Pforta, in which he gathered together all that can be discovered about the history of the Pforta academy, the so-called "German Eton", and in 1868–1869 he brought out a new edition of his work on Latin pronunciation.

From a very early period he had been attracted to the special study of Etruscan remains, and had at various times given occasional expression to his opinions on individual points; but it was not till 1870 that he had the opportunity of visiting Italy and completing his equipment for a formal treatment of the whole subject by personal inspection of the monuments. In 1874 appeared the first volume of Über die Sprache der Etrusker, in which with great ingenuity and erudition he endeavoured to prove that the Etruscan language was cognate with that of the Romans. Before the second volume (published posthumously under the editorship of Ernst Kuhn) had received the last touches of his hand, he was cut off in 1875 by a comparatively early death.

Influence on Friedrich Nietzsche
In Twilight of the Idols ("What I Owe to the Ancients") Friedrich Nietzsche, who was educated at the Schulpforta, pays homage to Corssen in discussion of his education and writing style:

 * My sense of style, for the epigram as a style, was awakened almost instantly when I came into contact with Sallust. I have not forgotten the surprise of my honored teacher, Corssen, when he had to give his worst Latin pupil the best grade—I had finished with one stroke.

title: Wilhelm Paul Corssen


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Question Time (TV programme)

Question Time is a topical debate programme, typically broadcast on BBC One at 10:45 pm on Thursdays. It is usually repeated on BBC Two (with British Sign Language) and on BBC Parliament later in the week. If there is a Leaders special, it would be broadcast simultaneously on BBC News. Question Time is also available on BBC iPlayer. Fiona Bruce currently chairs the show having succeeded David Dimbleby as presenter in January 2019.

Mentorn has produced the programme since 1998.

Origins
Question Time was first broadcast on Tuesday 25 September 1979, based on the BBC Radio 4 programme Any Questions? The first panel consisted of Labour MP Michael Foot, author Edna O'Brien, Conservative politician Teddy Taylor, and the Archbishop of Liverpool Derek Worlock.

Format
Question Time panels are typically composed of five public figures, "nearly always [including] a representative from the UK government and the official opposition." The panel also features "representatives from other political parties across the series, taking as [its] guide the level of electoral support at national level which each party enjoys."

High-profile journalists and authors, television and radio broadcasters, and comedians, join the panel, as do business leaders from well-known companies, and leading or expert academics, lawyers, police officers, and clerics.

With the exception of Margaret Thatcher, every British Prime Minister that has held office since the programme began in 1979 has appeared as a regular panelist at some point. Additionally, former Prime Ministers Edward Heath and James Callaghan also participated in panels, with Callaghan's single appearance coming in a special edition marking the resignation of Thatcher on 22 November 1990. Additionally, every leader of the Conservative party after Thatcher and prior to Rishi Sunak, Labour after Harold Wilson and the Liberal/Liberal Democrat parties after Clement Davies with the single exception of Jeremy Thorpe, have appeared as panelists.

Audience members are selected by the 'audience producer' based on age, gender, occupation, ethnicity, disability status, voting intention, voting history, and party membership. The audience members are "requested to come up with two questions, to be considered for the programme." The panel hears the questions for the first time, when the audience members ask them. Applicants are contacted on the Monday, Tuesday, or Wednesday before the programme, although due to a "high volume of requests", the team are unable to call everyone.

Question Time is usually recorded "as-live", and in a single-take, shortly before transmission. The programme is only edited on "very rare" occasions for legal or taste reasons, or because it over-runs. For example, The Observer newspaper reported in 1986 that "The BBC's lawyers ordered nine seconds of Question Time to be deleted by the old-fashioned method of simply cutting off the sound".

Sir Robin Day
Veteran broadcaster Sir Robin Day was the programme's first chair, presenting it for nearly 10 years until June 1989. Question Time soon gained popularity under Day's lead, with his quick wit and interrogation skills. His famous catchphrase when he had introduced the panel was: "There they are, and here we go."

The programme was mainly filmed at the Greenwood Theatre in London on the south side of London Bridge. Day's last appearance as presenter was broadcast from Paris on 12 July 1989. He was allowed to choose his own guests.

Peter Sissons
After Day retired, Peter Sissons took over and continued until 1993. The BBC decided to widen the programme's appeal by moving it around the country. The programme also changed its London location from the Greenwood Theatre to the Barbican Centre. Sissons' tenure as Question Time chair included three different editors. There were several problems during filming, including a bomb scare during a live recording, which resulted in the programme being taken off the air, and the death of an audience member who collapsed while recording.

The programme continued to enjoy good ratings during this period, notably on the day of Margaret Thatcher's resignation on 22 November 1990, which featured two different panels over two editions.

David Dimbleby
David Dimbleby succeeded Sissons as Question Time presenter in 1994, after the BBC held two pilot show auditions between Dimbleby and Jeremy Paxman, with two different audiences and two different panels. For a brief period under Dimbleby in the mid-1990s, there were a number of variations to the format, including the audience using voting keypads to take a poll of the audience at the end of the programme and Dimbleby getting out of his seat at intervals to question the audience.

Dimbleby presented Question Time for 25 years, the programme's longest-serving presenter, until his final programme, aged 80, on 13 December 2018.

Fiona Bruce
In December 2018, the BBC announced that Fiona Bruce would succeed Dimbleby as moderator. Bruce—along with Samira Ahmed, Victoria Derbyshire, Emily Maitlis, Nick Robinson, and Kirsty Wark—attended October 2018 auditions at London's James Allen's Girls' School. She presented her first Question Time in January 2019.

Guest presenters
Question Time has seen various presenters deputise for the main chair. Sir Ludovic Kennedy, Sue Lawley (the first woman to chair the programme), Bernard Levin (who is the only person to have been both programme chair and a panelist), and Donald MacCormick, all moderated in Day's place.

In November 2009, John Humphrys presented in lieu of Dimbleby, who had been "injured by a bullock at his farm" causing him "briefly to be knocked out." In June 2017, Nick Robinson presented a "Leaders Special" edition of Question Time. The programme was moved for news coverage of the London Bridge attack. Dimbleby was preparing for the General Election coverage. Victoria Derbyshire presented the 31 March 2022 edition of Question Time in place of Bruce.

Editors
The original 'producer' of Question Time when it began in 1979 was Barbara Maxwell. In 1983, Maxwell was promoted to 'Executive Producer' with Liz Elton becoming the show's producer/editor. At the same time, Ann Morley became the show's regular Director; putting the key production roles entirely in female control. Later in 1983, the role of Executive Producer was renamed 'Editor'. In 1986, Antonia Charlton and Anne Carragher replace Morley and Elton as the show's regular director and producer. After 11 years at the helm, Maxwell stepped down from the show in 1990, with her replacement James Hogan editing his first edition on 20 September 1990 Alexandra Henderson took over as editor on 12 September 1991 Christopher Capron became Series Editor in September 1994 Charlie Courtauld was editor from 1998 to 2000, leaving to join the Independent on Sunday as its comment editor. Nick Pisani was appointed in 2000, resigning abruptly in May 2005 after news was leaked that he had been offered a job as David Cameron's head of TV presentation. Ed Havard was made acting editor in May 2005 after Pisani left. During his time in charge the BBC offered a seat on the panel to Nick Griffin in 2009. He left when the programme's production base moved to Glasgow.

Gill Penlington, the ITV News political producer, was made interim editor in May 2008, when the BBC gave Ed Havard a year-long sabbatical.

SMS contributions
Viewers of the show can submit comments to the show via SMS; until October 2012 a selection of those comments was posted on Ceefax. Comments were edited and put to air by a team of four journalists based at Television Centre in London. The system displayed one message at a time, and usually showed several tens of messages throughout each hour-long episode. The system's popularity sprang from its mix of serious and light-hearted comments. On average, around 3,500 texts are received during each hour-long programme, although 12,000 texts were once recorded in one frantic programme in 2004. Quantity of texts is generally related to the composition of the panel.

Twitter
On 24 September 2009, the show launched its Twitter presence and the show's presenter has regularly announced its presence on Twitter since late 2009. Using the Twitter ID "@bbcquestiontime" it tweeted using the #bbcqt hashtag. By early 2010, this had become one of the UK's most active "Twitter backchannels" to a TV show. @bbcquestiontime claimed 10,000 tweets had been sent around the show on 7 October 2010. The show had over 40,000 followers on Twitter by October 2010 and this exceeded 50,000 on the evening of 3 February 2011.

On 9 June 2011, Question Time became one of the most-tweeted about shows of the week in the UK, with 5,000 tweets during the programme, with tweeting continuing through to the next day. In addition to the more sober analysis of the discussion, Question Time also has a parallel Twitter backchannel based on the spoof account Dimblebot - purportedly a robot version of Dimbleby - where the entire premise of the programme is claimed to be a demonstration of Dimbleby's ability to defeat the panel. It became clear during the riot special that David Dimbleby knows of the existence of Dimblebot and the associated Dimbledance. The @bbcquestiontime account now has over 500,000 followers.

In March 2020, Sayeeda Warsi, Baroness Warsi and MP Debbie Abrahams sent an open letter to the programme's runners, after Question Time uploaded anti-immigrants Twitter comments from a far-right supporter who allegedly also ran for the National Front and showed support for the English Defence League. Baroness Warsi and Abrahams wrote in their letter: "We understand the producers of the show seek out ‘controversial members of the audience – including those of far-right campaign groups – in an attempt to curry large ratings... By providing a platform for views that are racist or sexist, the institution is normalising them and contributing to the coarsening of public debate and the growing toxicity of our politics."

Venues
Question Time is filmed at a variety of different types of building, these have included: educational buildings, arts venues, and government buildings... airports, religious buildings, and prisons.

The programme has been broadcast from the Scottish Parliament, the Welsh Parliament, and Westminster Hall.

Heathrow Airport, Wormwood Scrubs Prison, and the cathedrals of St Paul's and Winchester, have all hosted Question Time.

LSO St Luke's and the Sage are among the arts venues to have held the programme and Queen Mary University of London and the University of Birmingham have also hosted Question Time.

2024 locations
Future locations and panellists sometimes change. Updates to locations are listed on the Question Time website or on-air.

Question Time held three General Election Leaders' Specials on 20, 24, and 28 June. They also held a special debate on 5 July.

 * 11 January: 🏴 Oxford
 * 18 January: 🏴 Peterborough
 * 25 January: 🏴 Gillingham
 * 1 February: 🏴 Glasgow
 * 8 February: 🏴 Nottingham
 * 15 February: 🏴 Lancaster
 * 22 February: 🏴 Maidenhead
 * 29 February: 🏴 Croydon
 * 7 March: 🏴 Cardiff
 * 14 March: 🏴 Liverpool
 * 21 March: 🏴 Middlesbrough
 * 18 April: 🏴 Buxton
 * 25 April: 🏴 London
 * 2 May: 🏴 Northstowe
 * 9 May: 🏴 Stoke-on-Trent
 * 16 May: 🏴 Aberdeen
 * 23 May: 🏴 Coventry
 * 30 May: 🏴 Epsom
 * 6 June: 🏴 Chester
 * 13 June: 🏴 Edinburgh
 * 20 June: 🏴 York
 * 24 June: 🏴 Cardiff
 * 27 June: 🏴 Birmingham
 * 28 June: 🏴 Birmingham
 * 5 July: 🏴 London
 * 11 July: 🏴 Colchester

Production
The show is recorded at different venues throughout the UK. Although, as part of plans to relocate BBC production around the UK, the main office of the programme will move to BBC Scotland in Glasgow, it is funded from the allocated BBC Scotland Licence fee allocation and is officially a "BBC Scotland production", as shown in the programme ending credits. However, the itinerant nature of the programme will continue.

Famous editions
In early 1981, David Steel declared his support in principle for "a marriage" between the Liberal Party and any party which might be formed by the Gang of Four; David Owen, who was also on the programme, said he could see advantages in an "electoral alliance" between them. This prefigured the period 1983–1987 when Owen and Steel were Leaders of the SDP–Liberal Alliance and tension grew over whether their deal was a prelude to a merger of the parties or merely a temporary electoral pact.

During the 1983 election campaign, Conservative Foreign Secretary Francis Pym was asked by an A-level student named Andy Davis about the implications of the Conservatives winning the election with a landslide victory. He began by casting doubt on the likelihood of this happening and then observed "I think landslides on the whole don't produce successful governments". Margaret Thatcher later wrote that the remark "struck a wrong note": "people drew the inference that he did not want us to win a large majority". Following the election (won by the Conservatives on a landslide) she sacked him as Foreign Secretary, partly because of his gaffe.

In a 1984 edition, Alan Clark, a junior government Minister at the time, was openly critical of a government decision to buy a foreign-made missile system, prompting guest host Sue Lawley to ask the audience, "Is there anyone here who wishes to defend the government on this, because its Minister doesn't?"

A 1994 edition was notable for a confrontation between Jeffrey Archer and the historian David Starkey over the age of homosexual consent. After arguing that 18 should be the age of consent, Archer was attacked by Starkey who told him: "Englishmen like you enjoy sitting on the fence so much because you enjoy the sensation." Archer replied "I enjoyed the very clever way you got a laugh ... I was not sitting on the fence and I was not compromising ... you don't have the right to doubt my beliefs and think just because you are an expert in this subject I don't have the right to say what I feel or get a cheap laugh out of it. I stand by 18 and I mean it."

The programme broadcast on 13 September 2001, which was devoted to the political implications of the September 11 attacks, featured many contributions from members of the audience who were anti-American, expressing the view that "the United States had it coming". The BBC received more than 2,000 complaints and later apologised to viewers for causing offence, stating that the edition should not have been broadcast live, but rather should have been recorded and edited.

In 2002, the editor of Private Eye, Ian Hislop, made an open attack on Jeffrey Archer, who had been imprisoned for perjury, when his wife Mary Archer was a fellow panellist. She was noticeably angry that the issue had been raised and criticised Hislop after the recording had finished.

In March 2007, an Iraq Special was broadcast, featuring Tony Benn, Benazir Bhutto, Des Browne, Liam Fox, Charles Kennedy and, via video link from Washington, D.C., John Bolton. The episode is particularly memorable for the clashes between Benn and Bolton.

On 11 October 2007, former editor of The Sun newspaper Kelvin MacKenzie appeared on the programme in Cheltenham and launched an attack on Scotland. During a debate about tax, MacKenzie claimed that "Scotland believes not in entrepreneurialism like London and the south east... Scots enjoy spending it (money) but they don't enjoy creating it, which is the opposite to down south." The comments came as part of an attack on Prime Minister Gordon Brown who MacKenzie said could not be trusted to manage the British economy because he was "a Scot" and a "socialist", and insisting that this was relevant to the debate. Fellow panellist Chuka Umunna from the think tank Compass called his comments "absolutely disgraceful", and booing and jeering were heard from the Cheltenham studio audience. The BBC received 350 complaints and MacKenzie's comments drew widespread criticism in both Scotland and England. On 3 July 2008, it was reported that the BBC Trust's editorial complaints unit had cleared the programme of any wrongdoing. Question Time then proceeded to broadcast the following question from Nick Hartley as part of the programme on the same evening: "After the media coverage of [Andy] Murray's rise and fall, are we now to infer that the English resent the Scots more than the Scots resent the English?" MacKenzie reappeared on the programme in Cardiff on 17 May 2012.

After he was elected to the European Parliament, Nick Griffin the leader of the British National Party was invited onto Question Time for the first time, to appear on 22 October 2009. The decision led to controversy and political debate. Hundreds of people protested outside BBC Television Centre as the edition was filmed; six people were arrested after 25 protesters forced their way into the main reception. The edition attracted eight million viewers, and also drew a large number of complaints as a result of its content. Griffin himself said that he would make a formal complaint to the BBC for the way he believed he was treated by the show's other guests and the audience, who he described as a "lynch mob."

An edition aired on 19 May 2011 was recorded at Wormwood Scrubs Prison in London. The episode was the first to feature prisoners as part of the audience, while panellists included Justice Secretary Ken Clarke who attempted to defend controversial remarks he had made earlier in the week about rape sentencing.

A special edition of the programme was aired on 11 August 2011 following the outbreak of rioting which had occurred during the previous weekend and earlier that week. Question Time had been off air for its annual summer break at the time and the edition was a scheduled at short notice due to the English riots.

An appearance by George Galloway on the edition of 5 February 2015 recorded in Finchley gained much negative comment before the broadcast. Inviting Galloway, a politician who has been outspoken about Israel, onto the programme was thought to be provocative and insensitive because Finchley has a large Jewish minority. Galloway, who was heckled during a discussion about antisemitism, thought he had been defamed by a question posed to him, which insinuated that he should share some of the blame for a rise in antisemitic incidents during 2014.

On 23 November 2017 the programme was shortened because an audience member became ill and could not be safely moved.

On 19 March 2020 the programme broadcast from Weston-super-Mare was the first without an audience, due to the COVID-19 pandemic. On 26 March 2020 the programme moved to a semi-permanent home at the IMG Studios at Stockley Park in London as a measure to prevent unnecessary travel during the Coronavirus outbreak, the show followed a different format with no audience, the number of panellists reduced to four and the removal of the desk table, with the panellists and host now sitting on chairs in a semi-circle, 2m apart from one another in order to observe social distancing rules. At the same time, the programme moved to a new, temporary timeslot of 8pm, so that it could be broadcast live and allow viewers to submit their own questions to be answered on the programme.

Four-time Formula One World Champion and Grand Prix Drivers' Association, the trade union for F1, director Sebastian Vettel appeared on the 12 May 2022 edition, which was broadcast from Hackney.

Audience figures
The highest audience figures to date were recorded when Nick Griffin of the BNP appeared in an episode on 22 October 2009; the audience reaching 8.3 million viewers.

On 14 May 2009, Question Time discussed the MPs' expenses row, with audience members heckling guest panellists Menzies Campbell and Margaret Beckett, the Labour MP, who was booed by the audience for insisting that her expenses were her privilege. The TV audience reached 3.8 million.

3.4 million people watched in 2003 at the start of the war on Iraq.

Similar programmes

 * A Welsh-language version, Pawb a'i Farn, has been broadcast on S4C since 1993.
 * In 1994, BBC Scotland launched their own local debate show called Words with Wark which was broadcast on BBC One Scotland usually on the first Thursday of every month and this was presented by Kirsty Wark. The programme was axed in 1998.
 * Until 2010, BBC One Northern Ireland replaced Question Time with the more local debate show Let's Talk at least once a month hosted by Mark Carruthers, but this show has been axed and brought under the Spotlight brand. It is now shown once a month on Tuesday night with Noel Thompson. BBC One NI have their own political show called The View: this is broadcast live from 10.35pm to 11.20pm, presented by Mark Carruthers, and is followed by Question Time. If Question Time is made in Northern Ireland.
 * BBC World produces an Indian version of the programme for such viewers.
 * The Irish broadcaster RTÉ produced a similar show, Questions and Answers, which ran from 1986 to 2009, and was replaced by The Frontline, which is of a similar format.
 * In March 2010, Dermot O'Leary hosted a spinoff edition of the show, which was broadcast on BBC Three. It was called First Time Voters' Question Time, and the show was aimed at first time voters. This version of the programme was later commissioned on a permanent, monthly basis on BBC Three, to now be hosted by Richard Bacon, and re-titled Young Voters' Question Time. He was replaced by Jake Humphrey then by Rick Edwards with Tina Daheley, and the show was renamed Free Speech which goes out every month.
 * In 2007, the BBC commissioned The Big Questions, a new programme with a similar format to Question Time, which focuses on ethical and religious issues. It is broadcast on BBC One on Sunday mornings between 10am and 11am. Both programmes are produced by Mentorn Media.
 * In 2008, the Australian Broadcasting Corporation launched a similar, Australian version of the programme, called Q&A. Airing live weekly, it has become a critical success, achieving extremely positive ratings for the ABC in Australia, with a wide audience from a range of demographics not all of which are normally noted for their interest in the Australian political scene.
 * In 2011, Azerbaijan launched its own version of the programme. Open Talk Açıq söhbət is a weekly debate ANS TV television programme in Azerbaijan, based on Question Time. The show features political leaders as well as other public figures. Open Talk is presented by Sevinj Osmanqizi.

Schools edition
Several schools editions have been broadcast:
 * 20 June 2005, with a panel of Tony Benn, Justine Greening, Lembit Öpik, June Sarpong and Otis Ferry.
 * 6 July 2006, with a twenty-year-old student joining David Miliband, Richard Madeley, Lord Coe and Julia Goldsworthy.
 * 5 July 2007, an 18-year-old student joined a panel of Ed Miliband, Sayeeda Warsi, Davina McCall and Douglas Murray.
 * 9 July 2009, one of the panellists was an eighteen-year-old student. Other panellists were Andy Burnham, Jeremy Hunt, Sarah Teather and Shami Chakrabarti.

Other sources

 * Bailey, Ric (26 January 2005) "NewsWatch: Question Time for Question Time" BBC News Retrieved 9 July 2005.
 * "The Best of Question Time" BBC News Retrieved 9 July 2005.
 * Guyon, Janet (29 November 2001) "Fortune: The New Future—The American Way" Mutual of America Retrieved 9 July 2005.
 * Shawcross, William (17 September 2001) "Stop This Racism" The Guardian Retrieved 9 July 2005.

title: Question Time (TV programme)


text:
User:Tostadora

About Me
I'm a 21 years old Spanish IT student. I love music, specially the Metal genre. My current work in Wikipedia is completing description from Metal Bands I know. My english is not very good, so please correct my pages :).

I'm currently working in the spanish wikipedia: es:Usuario:Tostadora

Pages I worked On

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title: User:Tostadora


text:
Veno Pilon

Veno Pilon (22 September 1896 – 23 September 1970) was a Slovene expressionist painter, graphic artist and photographer.

Biography
Pilon was born in Ajdovščina, then part of the Austro-Hungarian province of Gorizia and Gradisca (now in Slovenia). After he had finished the Gorizia Grammar School, he was drafted by the Austro-Hungarian Army during World War I. He fought on the Eastern front and was captured by the Russian army. He later described his experience as a prisoner of war in the autobiography Na robu ("On the Edge"). He returned to Ajdovščina in 1919, where he took up painting.

In the late 1920s Pilon moved to Paris, where he explored photography.

He died in Ajdovščina.

title: Veno Pilon


text:
User:Omegatron/Gallery

Real gallery is now at Commons: commons:User:Omegatron/Gallery. This is a list of the images that still remain on en for whatever reason. (Keeping track of.)

title: User:Omegatron/Gallery


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Learning classifier system

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm in evolutionary computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy). This approach allows complex solution spaces to be broken up into smaller, simpler parts for the reinforcement learning that is inside artificial intelligence research.

The founding concepts behind learning classifier systems came from attempts to model complex adaptive systems, using rule-based agents to form an artificial cognitive system (i.e. artificial intelligence).

Methodology
The architecture and components of a given learning classifier system can be quite variable. It is useful to think of an LCS as a machine consisting of several interacting components. Components may be added or removed, or existing components modified/exchanged to suit the demands of a given problem domain (like algorithmic building blocks) or to make the algorithm flexible enough to function in many different problem domains. As a result, the LCS paradigm can be flexibly applied to many problem domains that call for machine learning. The major divisions among LCS implementations are as follows: (1) Michigan-style architecture vs. Pittsburgh-style architecture, (2) reinforcement learning vs. supervised learning, (3) incremental learning vs. batch learning, (4) online learning vs. offline learning, (5) strength-based fitness vs. accuracy-based fitness, and (6) complete action mapping vs best action mapping. These divisions are not necessarily mutually exclusive. For example, XCS, the best known and best studied LCS algorithm, is Michigan-style, was designed for reinforcement learning but can also perform supervised learning, applies incremental learning that can be either online or offline, applies accuracy-based fitness, and seeks to generate a complete action mapping.

Elements of a generic LCS algorithm
Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm. For simplicity let us focus on Michigan-style architecture with supervised learning. See the illustrations on the right laying out the sequential steps involved in this type of generic LCS.

Environment
The environment is the source of data upon which an LCS learns. It can be an offline, finite training dataset (characteristic of a data mining, classification, or regression problem), or an online sequential stream of live training instances. Each training instance is assumed to include some number of features (also referred to as attributes, or independent variables), and a single endpoint of interest (also referred to as the class, action, phenotype, prediction, or dependent variable). Part of LCS learning can involve feature selection, therefore not all of the features in the training data need to be informative. The set of feature values of an instance is commonly referred to as the state. For simplicity let's assume an example problem domain with Boolean/binary features and a Boolean/binary class. For Michigan-style systems, one instance from the environment is trained on each learning cycle (i.e. incremental learning). Pittsburgh-style systems perform batch learning, where rule sets are evaluated in each iteration over much or all of the training data.

Rule/classifier/population
A rule is a context dependent relationship between state values and some prediction. Rules typically take the form of an {IF:THEN} expression, (e.g. {IF 'condition' THEN 'action'}, or as a more specific example, {IF 'red' AND 'octagon' THEN 'stop-sign'}). A critical concept in LCS and rule-based machine learning alike, is that an individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Think of a rule as a "local-model" of the solution space.

Rules can be represented in many different ways to handle different data types (e.g. binary, discrete-valued, ordinal, continuous-valued). Given binary data LCS traditionally applies a ternary rule representation (i.e. rules can include either a 0, 1, or '#' for each feature in the data). The 'don't care' symbol (i.e. '#') serves as a wild card within a rule's condition allowing rules, and the system as a whole to generalize relationships between features and the target endpoint to be predicted. Consider the following rule (#1###0 ~ 1) (i.e. condition ~ action). This rule can be interpreted as: IF the second feature = 1 AND the sixth feature = 0 THEN the class prediction = 1. We would say that the second and sixth features were specified in this rule, while the others were generalized. This rule, and the corresponding prediction are only applicable to an instance when the condition of the rule is satisfied by the instance. This is more commonly referred to as matching. In Michigan-style LCS, each rule has its own fitness, as well as a number of other rule-parameters associated with it that can describe the number of copies of that rule that exist (i.e. the numerosity), the age of the rule, its accuracy, or the accuracy of its reward predictions, and other descriptive or experiential statistics. A rule along with its parameters is often referred to as a classifier. In Michigan-style systems, classifiers are contained within a population [P] that has a user defined maximum number of classifiers. Unlike most stochastic search algorithms (e.g. evolutionary algorithms), LCS populations start out empty (i.e. there is no need to randomly initialize a rule population). Classifiers will instead be initially introduced to the population with a covering mechanism.

In any LCS, the trained model is a set of rules/classifiers, rather than any single rule/classifier. In Michigan-style LCS, the entire trained (and optionally, compacted) classifier population forms the prediction model.

Matching
One of the most critical and often time-consuming elements of an LCS is the matching process. The first step in an LCS learning cycle takes a single training instance from the environment and passes it to [P] where matching takes place. In step two, every rule in [P] is now compared to the training instance to see which rules match (i.e. are contextually relevant to the current instance). In step three, any matching rules are moved to a match set [M]. A rule matches a training instance if all feature values specified in the rule condition are equivalent to the corresponding feature value in the training instance. For example, assuming the training instance is (001001 ~ 0), these rules would match: (###0## ~ 0), (00###1 ~ 0), (#01001 ~ 1), but these rules would not (1##### ~ 0), (000##1 ~ 0), (#0#1#0 ~ 1). Notice that in matching, the endpoint/action specified by the rule is not taken into consideration. As a result, the match set may contain classifiers that propose conflicting actions. In the fourth step, since we are performing supervised learning, [M] is divided into a correct set [C] and an incorrect set [I]. A matching rule goes into the correct set if it proposes the correct action (based on the known action of the training instance), otherwise it goes into [I]. In reinforcement learning LCS, an action set [A] would be formed here instead, since the correct action is not known.

Covering
At this point in the learning cycle, if no classifiers made it into either [M] or [C] (as would be the case when the population starts off empty), the covering mechanism is applied (fifth step). Covering is a form of online smart population initialization. Covering randomly generates a rule that matches the current training instance (and in the case of supervised learning, that rule is also generated with the correct action. Assuming the training instance is (001001 ~ 0), covering might generate any of the following rules: (#0#0## ~ 0), (001001 ~ 0), (#010## ~ 0). Covering not only ensures that each learning cycle there is at least one correct, matching rule in [C], but that any rule initialized into the population will match at least one training instance. This prevents LCS from exploring the search space of rules that do not match any training instances.

Parameter updates/credit assignment/learning
In the sixth step, the rule parameters of any rule in [M] are updated to reflect the new experience gained from the current training instance. Depending on the LCS algorithm, a number of updates can take place at this step. For supervised learning, we can simply update the accuracy/error of a rule. Rule accuracy/error is different than model accuracy/error, since it is not calculated over the entire training data, but only over all instances that it matched. Rule accuracy is calculated by dividing the number of times the rule was in a correct set [C] by the number of times it was in a match set [M]. Rule accuracy can be thought of as a 'local accuracy'. Rule fitness is also updated here, and is commonly calculated as a function of rule accuracy. The concept of fitness is taken directly from classic genetic algorithms. Be aware that there are many variations on how LCS updates parameters in order to perform credit assignment and learning.

Subsumption
In the seventh step, a subsumption mechanism is typically applied. Subsumption is an explicit generalization mechanism that merges classifiers that cover redundant parts of the problem space. The subsuming classifier effectively absorbs the subsumed classifier (and has its numerosity increased). This can only happen when the subsuming classifier is more general, just as accurate, and covers all of the problem space of the classifier it subsumes.

Rule discovery/genetic algorithm
In the eighth step, LCS adopts a highly elitist genetic algorithm (GA) which will select two parent classifiers based on fitness (survival of the fittest). Parents are selected from [C] typically using tournament selection. Some systems have applied roulette wheel selection or deterministic selection, and have differently selected parent rules from either [P] - panmictic selection, or from [M]). Crossover and mutation operators are now applied to generate two new offspring rules. At this point, both the parent and offspring rules are returned to [P]. The LCS genetic algorithm is highly elitist since each learning iteration, the vast majority of the population is preserved. Rule discovery may alternatively be performed by some other method, such as an estimation of distribution algorithm, but a GA is by far the most common approach. Evolutionary algorithms like the GA employ a stochastic search, which makes LCS a stochastic algorithm. LCS seeks to cleverly explore the search space, but does not perform an exhaustive search of rule combinations, and is not guaranteed to converge on an optimal solution.

Deletion
The last step in a generic LCS learning cycle is to maintain the maximum population size. The deletion mechanism will select classifiers for deletion (commonly using roulette wheel selection). The probability of a classifier being selected for deletion is inversely proportional to its fitness. When a classifier is selected for deletion, its numerosity parameter is reduced by one. When the numerosity of a classifier is reduced to zero, it is removed entirely from the population.

Training
LCS will cycle through these steps repeatedly for some user defined number of training iterations, or until some user defined termination criteria have been met. For online learning, LCS will obtain a completely new training instance each iteration from the environment. For offline learning, LCS will iterate through a finite training dataset. Once it reaches the last instance in the dataset, it will go back to the first instance and cycle through the dataset again.

Rule compaction
Once training is complete, the rule population will inevitably contain some poor, redundant and inexperienced rules. It is common to apply a rule compaction, or condensation heuristic as a post-processing step. This resulting compacted rule population is ready to be applied as a prediction model (e.g. make predictions on testing instances), and/or to be interpreted for knowledge discovery.

Prediction
Whether or not rule compaction has been applied, the output of an LCS algorithm is a population of classifiers which can be applied to making predictions on previously unseen instances. The prediction mechanism is not part of the supervised LCS learning cycle itself, however it would play an important role in a reinforcement learning LCS learning cycle. For now we consider how the prediction mechanism can be applied for making predictions to test data. When making predictions, the LCS learning components are deactivated so that the population does not continue to learn from incoming testing data. A test instance is passed to [P] where a match set [M] is formed as usual. At this point the match set is differently passed to a prediction array. Rules in the match set can predict different actions, therefore a voting scheme is applied. In a simple voting scheme, the action with the strongest supporting 'votes' from matching rules wins, and becomes the selected prediction. All rules do not get an equal vote. Rather the strength of the vote for a single rule is commonly proportional to its numerosity and fitness. This voting scheme and the nature of how LCS's store knowledge, suggests that LCS algorithms are implicitly ensemble learners.

Interpretation
Individual LCS rules are typically human readable IF:THEN expression. Rules that constitute the LCS prediction model can be ranked by different rule parameters and manually inspected. Global strategies to guide knowledge discovery using statistical and graphical have also been proposed. With respect to other advanced machine learning approaches, such as artificial neural networks, random forests, or genetic programming, learning classifier systems are particularly well suited to problems that require interpretable solutions.

Early years
John Henry Holland was best known for his work popularizing genetic algorithms (GA), through his ground-breaking book "Adaptation in Natural and Artificial Systems" in 1975 and his formalization of Holland's schema theorem. In 1976, Holland conceptualized an extension of the GA concept to what he called a "cognitive system", and provided the first detailed description of what would become known as the first learning classifier system in the paper "Cognitive Systems based on Adaptive Algorithms". This first system, named Cognitive System One (CS-1) was conceived as a modeling tool, designed to model a real system (i.e. environment) with unknown underlying dynamics using a population of human readable rules. The goal was for a set of rules to perform online machine learning to adapt to the environment based on infrequent payoff/reward (i.e. reinforcement learning) and apply these rules to generate a behavior that matched the real system. This early, ambitious implementation was later regarded as overly complex, yielding inconsistent results.

Beginning in 1980, Kenneth de Jong and his student Stephen Smith took a different approach to rule-based machine learning with (LS-1), where learning was viewed as an offline optimization process rather than an online adaptation process. This new approach was more similar to a standard genetic algorithm but evolved independent sets of rules. Since that time LCS methods inspired by the online learning framework introduced by Holland at the University of Michigan have been referred to as Michigan-style LCS, and those inspired by Smith and De Jong at the University of Pittsburgh have been referred to as Pittsburgh-style LCS. In 1986, Holland developed what would be considered the standard Michigan-style LCS for the next decade.

Other important concepts that emerged in the early days of LCS research included (1) the formalization of a bucket brigade algorithm (BBA) for credit assignment/learning, (2) selection of parent rules from a common 'environmental niche' (i.e. the match set [M]) rather than from the whole population [P], (3) covering, first introduced as a create operator, (4) the formalization of an action set [A], (5) a simplified algorithm architecture, (6) strength-based fitness, (7) consideration of single-step, or supervised learning problems and the introduction of the correct set [C], (8) accuracy-based fitness (9) the combination of fuzzy logic with LCS (which later spawned a lineage of fuzzy LCS algorithms), (10) encouraging long action chains and default hierarchies for improving performance on multi-step problems,  (11) examining latent learning (which later inspired a new branch of anticipatory classifier systems (ACS) ), and (12) the introduction of the first Q-learning-like credit assignment technique. While not all of these concepts are applied in modern LCS algorithms, each were landmarks in the development of the LCS paradigm.

The revolution
Interest in learning classifier systems was reinvigorated in the mid 1990s largely due to two events; the development of the Q-Learning algorithm for reinforcement learning, and the introduction of significantly simplified Michigan-style LCS architectures by Stewart Wilson. Wilson's Zeroth-level Classifier System (ZCS) focused on increasing algorithmic understandability based on Hollands standard LCS implementation. This was done, in part, by removing rule-bidding and the internal message list, essential to the original BBA credit assignment, and replacing it with a hybrid BBA/Q-Learning strategy. ZCS demonstrated that a much simpler LCS architecture could perform as well as the original, more complex implementations. However, ZCS still suffered from performance drawbacks including the proliferation of over-general classifiers.

In 1995, Wilson published his landmark paper, "Classifier fitness based on accuracy" in which he introduced the classifier system XCS. XCS took the simplified architecture of ZCS and added an accuracy-based fitness, a niche GA (acting in the action set [A]), an explicit generalization mechanism called subsumption, and an adaptation of the Q-Learning credit assignment. XCS was popularized by its ability to reach optimal performance while evolving accurate and maximally general classifiers as well as its impressive problem flexibility (able to perform both reinforcement learning and supervised learning). XCS later became the best known and most studied LCS algorithm and defined a new family of accuracy-based LCS. ZCS alternatively became synonymous with strength-based LCS. XCS is also important, because it successfully bridged the gap between LCS and the field of reinforcement learning. Following the success of XCS, LCS were later described as reinforcement learning systems endowed with a generalization capability. Reinforcement learning typically seeks to learn a value function that maps out a complete representation of the state/action space. Similarly, the design of XCS drives it to form an all-inclusive and accurate representation of the problem space (i.e. a complete map) rather than focusing on high payoff niches in the environment (as was the case with strength-based LCS). Conceptually, complete maps don't only capture what you should do, or what is correct, but also what you shouldn't do, or what's incorrect. Differently, most strength-based LCSs, or exclusively supervised learning LCSs seek a rule set of efficient generalizations in the form of a best action map (or a partial map). Comparisons between strength vs. accuracy-based fitness and complete vs. best action maps have since been examined in greater detail.

In the wake of XCS
XCS inspired the development of a whole new generation of LCS algorithms and applications. In 1995, Congdon was the first to apply LCS to real-world epidemiological investigations of disease followed closely by Holmes who developed the BOOLE++, EpiCS, and later EpiXCS for epidemiological classification. These early works inspired later interest in applying LCS algorithms to complex and large-scale data mining tasks epitomized by bioinformatics applications. In 1998, Stolzmann introduced anticipatory classifier systems (ACS) which included rules in the form of 'condition-action-effect, rather than the classic 'condition-action' representation. ACS was designed to predict the perceptual consequences of an action in all possible situations in an environment. In other words, the system evolves a model that specifies not only what to do in a given situation, but also provides information of what will happen after a specific action will be executed. This family of LCS algorithms is best suited to multi-step problems, planning, speeding up learning, or disambiguating perceptual aliasing (i.e. where the same observation is obtained in distinct states but requires different actions). Butz later pursued this anticipatory family of LCS developing a number of improvements to the original method. In 2002, Wilson introduced XCSF, adding a computed action in order to perform function approximation. In 2003, Bernado-Mansilla introduced a sUpervised Classifier System (UCS), which specialized the XCS algorithm to the task of supervised learning, single-step problems, and forming a best action set. UCS removed the reinforcement learning strategy in favor of a simple, accuracy-based rule fitness as well as the explore/exploit learning phases, characteristic of many reinforcement learners. Bull introduced a simple accuracy-based LCS (YCS) and a simple strength-based LCS Minimal Classifier System (MCS) in order to develop a better theoretical understanding of the LCS framework. Bacardit introduced GAssist and BioHEL, Pittsburgh-style LCSs designed for data mining and scalability to large datasets in bioinformatics applications. In 2008, Drugowitsch published the book titled "Design and Analysis of Learning Classifier Systems" including some theoretical examination of LCS algorithms. Butz introduced the first rule online learning visualization within a GUI for XCSF (see the image at the top of this page). Urbanowicz extended the UCS framework and introduced ExSTraCS, explicitly designed for supervised learning in noisy problem domains (e.g. epidemiology and bioinformatics). ExSTraCS integrated (1) expert knowledge to drive covering and genetic algorithm towards important features in the data, (2) a form of long-term memory referred to as attribute tracking, allowing for more efficient learning and the characterization of heterogeneous data patterns, and (3) a flexible rule representation similar to Bacardit's mixed discrete-continuous attribute list representation. Both Bacardit and Urbanowicz explored statistical and visualization strategies to interpret LCS rules and perform knowledge discovery for data mining. Browne and Iqbal explored the concept of reusing building blocks in the form of code fragments and were the first to solve the 135-bit multiplexer benchmark problem by first learning useful building blocks from simpler multiplexer problems. ExSTraCS 2.0 was later introduced to improve Michigan-style LCS scalability, successfully solving the 135-bit multiplexer benchmark problem for the first time directly. The n-bit multiplexer problem is highly epistatic and heterogeneous, making it a very challenging machine learning task.

Michigan-Style Learning Classifier System
Michigan-Style LCSs are characterized by a population of rules where the genetic algorithm operates at the level of individual rules and the solution is represented by the entire rule population. Michigan style systems also learn incrementally which allows them to perform both reinforcement learning and supervised learning, as well as both online and offline learning. Michigan-style systems have the advantage of being applicable to a greater number of problem domains, and the unique benefits of incremental learning.

Pittsburgh-Style Learning Classifier System
Pittsburgh-Style LCSs are characterized by a population of variable length rule-sets where each rule-set is a potential solution. The genetic algorithm typically operates at the level of an entire rule-set. Pittsburgh-style systems can also uniquely evolve ordered rule lists, as well as employ a default rule. These systems have the natural advantage of identifying smaller rule sets, making these systems more interpretable with regards to manual rule inspection.

Hybrid systems
Systems that seek to combine key strengths of both systems have also been proposed.

Advantages

 * Adaptive: They can acclimate to a changing environment in the case of online learning.
 * Model free: They make limited assumptions about the environment, or the patterns of association within the data.
 * They can model complex, epistatic, heterogeneous, or distributed underlying patterns without relying on prior knowledge.
 * They make no assumptions about the number of predictive vs. non-predictive features in the data.
 * Ensemble Learner: No single model is applied to a given instance that universally provides a prediction. Instead a relevant and often conflicting set of rules contribute a 'vote' which can be interpreted as a fuzzy prediction.
 * Stochastic Learner: Non-deterministic learning is advantageous in large-scale or high complexity problems where deterministic or exhaustive learning becomes intractable.
 * Implicitly Multi-objective: Rules evolve towards accuracy with implicit and explicit pressures encouraging maximal generality/simplicity. This implicit generalization pressure is unique to LCS. Effectively, more general rules, will appear more often in match sets.  In turn, they have a more frequent opportunity to be selected as parents, and pass on their more general (genomes) to offspring rules.
 * Interpretable:In the interest of data mining and knowledge discovery individual LCS rules are logical, and can be made to be human interpretable IF:THEN statements. Effective strategies have also been introduced to allow for global knowledge discovery identifying significant features, and patterns of association from the rule population as a whole.
 * Flexible application
 * Single or multi-step problems
 * Supervised, Reinforcement or Unsupervised Learning
 * Binary Class and Multi-Class Classification
 * Regression
 * Discrete or continuous features (or some mix of both types)
 * Clean or noisy problem domains
 * Balanced or imbalanced datasets.
 * Accommodates missing data (i.e. missing feature values in training instances)

Disadvantages

 * Limited Software Availability: There are a limited number of open source, accessible LCS implementations, and even fewer that are designed to be user friendly or accessible to machine learning practitioners.
 * Interpretation: While LCS algorithms are certainly more interpretable than some advanced machine learners, users must interpret a set of rules (sometimes large sets of rules to comprehend the LCS model.). Methods for rule compaction, and interpretation strategies remains an area of active research.
 * Theory/Convergence Proofs: There is a relatively small body of theoretical work behind LCS algorithms.  This is likely due to their relative algorithmic complexity (applying a number of interacting components) as well as their stochastic nature.
 * Overfitting: Like any machine learner, LCS can suffer from overfitting despite implicit and explicit generalization pressures.
 * Run Parameters: LCSs often have many run parameters to consider/optimize. Typically, most parameters can be left to the community determined defaults with the exception of two critical parameters: Maximum rule population size, and the maximum number of learning iterations.  Optimizing these parameters are likely to be very problem dependent.
 * Notoriety: Despite their age, LCS algorithms are still not widely known even in machine learning communities.  As a result, LCS algorithms are rarely considered in comparison to other established machine learning approaches.  This is likely due to the following factors:  (1) LCS is a relatively complicated algorithmic approach, (2) LCS, rule-based modeling is a different paradigm of modeling than almost all other machine learning approaches. (3) LCS software implementations are not as common.
 * Computationally Expensive: While certainly more feasible than some exhaustive approaches, LCS algorithms can be computationally expensive.  For simple, linear learning problems there is no need to apply an LCS.  LCS algorithms are best suited to complex problem spaces, or problem spaces in which little prior knowledge exists.

Problem domains

 * Adaptive-control
 * Data Mining
 * Engineering Design
 * Feature Selection
 * Function Approximation
 * Game-Play
 * Image Classification
 * Knowledge Handling
 * Medical Diagnosis
 * Modeling
 * Navigation
 * Optimization
 * Prediction
 * Querying
 * Robotics
 * Routing
 * Rule-Induction
 * Scheduling
 * Strategy

Terminology
The name, "Learning Classifier System (LCS)", is a bit misleading since there are many machine learning algorithms that 'learn to classify' (e.g. decision trees, artificial neural networks), but are not LCSs. The term 'rule-based machine learning (RBML)' is useful, as it more clearly captures the essential 'rule-based' component of these systems, but it also generalizes to methods that are not considered to be LCSs (e.g. association rule learning, or artificial immune systems). More general terms such as, 'genetics-based machine learning', and even 'genetic algorithm' have also been applied to refer to what would be more characteristically defined as a learning classifier system. Due to their similarity to genetic algorithms, Pittsburgh-style learning classifier systems are sometimes generically referred to as 'genetic algorithms'. Beyond this, some LCS algorithms, or closely related methods, have been referred to as 'cognitive systems', 'adaptive agents', 'production systems', or generically as a 'classifier system'. This variation in terminology contributes to some confusion in the field.

Up until the 2000s nearly all learning classifier system methods were developed with reinforcement learning problems in mind. As a result, the term ‘learning classifier system’ was commonly defined as the combination of ‘trial-and-error’ reinforcement learning with the global search of a genetic algorithm. Interest in supervised learning applications, and even unsupervised learning have since broadened the use and definition of this term.

Video tutorial

 * Learning Classifier Systems in a Nutshell - (2016) Go inside a basic LCS algorithm to learn their components and how they work.

Webpages

 * LCS & GBML Central
 * UWE Learning Classifier Research Group
 * Prediction Dynamics

title: Learning classifier system


text:
Gosia Dobrowolska

Małgorzata Dobrowolska (born 2 June 1958, in Kamienna Góra), known as Gosia Dobrowolska, is a Polish-born Australian actress.

Biography
She has one brother, Janusz Dobrowolski, who also made a minor appearance in the 1984 film, Silver City.

After moving to Australia in the 1980s, Dobrowolska's first Australian film was Silver City, where she played a Polish immigrant.

Paul Cox specifically wrote the 1991 film A Woman's Tale for Sheila Florance. Directed by Cox, Dobrowolska plays a nurse who befriends an elderly woman (Florance) dying of cancer.

title: Gosia Dobrowolska


text:
Suina

Suina (also known as Suiformes) is a suborder of omnivorous, non-ruminant artiodactyl mammals that includes the domestic pig and peccaries. A member of this clade is known as a suine. Suina includes the family Suidae, termed suids, known in English as pigs or swine, as well as the family Tayassuidae, termed tayassuids or peccaries. Suines are largely native to Africa, South America, and Southeast Asia, with the exception of the wild boar, which is additionally native to Europe and Asia and introduced to North America and Australasia, including widespread use in farming of the domestic pig subspecies. Suines range in size from the 55 cm (22 in) long pygmy hog to the 210 cm (83 in) long giant forest hog, and are primarily found in forest, shrubland, and grassland biomes, though some can be found in deserts, wetlands, or coastal regions. Most species do not have population estimates, though approximately two billion domestic pigs are used in farming, while several species are considered endangered or critically endangered with populations as low as 100. One species, Heude's pig, is considered by the International Union for Conservation of Nature to have gone extinct in the 20th century.

Classification
Suina's placement within Artiodactyla can be represented in the following cladogram:

The suborder Suina consists of 21 extant species in nine genera. These are split between the Suidae family, containing 18 species belonging to 6 genera, and the Tayassuidae family, containing 3 species in 3 genera. This does not include hybrid species such as boar–pig hybrids or extinct prehistoric species. Additionally, one species, Heude's pig, went extinct in the 20th century.

 * Family Suidae (Pigs)
 * Genus Babyrousa: four species
 * Genus Hylochoerus: one species
 * Genus Phacochoerus: two species
 * Genus Porcula: one species
 * Genus Potamochoerus: two species
 * Genus Sus: nine species
 * Family Tayassuidae (Peccaries)
 * Genus Catagonus: one species
 * Genus Dicotyles: one species
 * Genus Tayassu: one species

title: Suina


text:
Franz Felix Adalbert Kuhn

Franz Felix Adalbert Kuhn (19 November 1812 – 5 May 1881) was a German philologist and folklorist.

Kuhn was born in Königsberg in Brandenburg's Neumark region. From 1841, he was connected with the Köllnisches Gymnasium at Berlin, of which he was appointed director in 1870. Kuhn was the founder of a new school of comparative mythology, based upon comparative philology. Inspired by Jakob Grimm's Deutsche Mythologie, he first devoted himself to German stories and legends, and published Märkische Sagen und Märchen (1842), Norddeutsche Sagen, Märchen und Gebräuche (1848), and Sagen, Gebräuche und Märchen aus Westfalen (1859).

But it is on Kuhn's researches into the language and history of the Indo-Germanic peoples as a whole that his reputation is founded. His chief works in this connection are Zur ältesten Geschichte der Indogermanischen Völker (1845), in which he endeavoured to give an account of the earliest civilization of the Indo-Germanic peoples before their separation into different families, by comparing and analysing the original meaning of the words and stems common to the different languages; Die Herabkunft des Feuers und des Göttertranks (1859; new edition by Ernst Kuhn, under title of Mythologische Studien, 1886); and Über Entwicklungsstufen der Mythenbildung (1873), in which he maintained that the origin of myths was to be looked for in the domain of language, and that their most essential factors were polysemy and homonymy. Kuhn was also the editor of the Zeitschrift für vergleichende Sprachforschung auf dem Gebiete der Indogermanischen Sprachen, which was at his time the standard periodical on the subject.

Kuhn died in Berlin. See obituary notice by C. F. H. Bruchmann in Bursian's Biographisches Jahrbuch (1881) and J. Schmidt in the above Zeitschrift, xxvi. n.s. 6.

title: Franz Felix Adalbert Kuhn