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Bayer 04 Leverkusen

Bayer 04 Leverkusen, officially known as Bayer 04 Leverkusen Fußball GmbH and commonly known as Bayer Leverkusen or simply Leverkusen, is a German professional football club based in Leverkusen, North Rhine-Westphalia. It competes in the Bundesliga, the top tier of German football, and plays its home matches at the BayArena.

Founded in 1904 by employees of the pharmaceutical company Bayer (whose headquarters are in Leverkusen and from which the club draws its name), the club was formerly the best-known department of TSV Bayer 04 Leverkusen and RTHC Bayer Leverkusen, sports clubs whose members participate in athletics, gymnastics, basketball, field handball, rowing, tennis and hockey. In 1999, the football department was separated from the sports club. Bayer Leverkusen's main colours are red and black, which feature across their playing kits and on their badge, and their local rivals are 1. FC Köln, Borussia Mönchengladbach and Fortuna Düsseldorf.

Bayer Leverkusen were promoted to the Bundesliga in 1979 and won their first top-flight honour, the UEFA Cup, in 1988, then the DFB Pokal in 1993. The club infamously finished runners-up across three competitions in 2002, including the UEFA Champions League. After more than 30 years without silverware, the club won their first ever Bundesliga title in 2024 as well as the DFB Pokal, becoming the first German team to do so unbeaten, the first German team to complete an unbeaten domestic double, and set the European record for consecutive unbeaten competitive games (51).

Origins and early years
On 27 November 1903, Wilhelm Hauschild wrote a letter – signed by 180 of his fellow workers – to his employer, the Friedrich Bayer and Co., seeking the company's support in starting a sports club. The company agreed to support the initiative, and on 1 July 1904 Turn- und Spielverein Bayer 04 Leverkusen was founded as a works team. On 31 May 1907, a separate football department was formed within the club. In the culture of sports in Germany at the time, there was significant animosity between gymnasts and other types of athletes. Eventually this contributed to a split within the club: on 8 June 1928, the footballers formed a separate association – Sportvereinigung Bayer 04 Leverkusen – that also included the handball and fistball players, athletics, and boxing, while the gymnasts carried on as TuS Bayer 04 Leverkusen. SV Bayer 04 Leverkusen took with them the club's traditional colours of red and black, with the gymnasts adopting blue and yellow.

Through this period, and into the 1930s, SV Bayer 04 Leverkusen played third and fourth division football. In 1936, they earned promotion to the second highest class of play of the period. That was also the year that the club wore the "Bayer" cross, still visible on their kits, for the first time. They made their first appearance in upper league play in 1951, in the Oberliga West and played there until 1956, after which they were relegated.

SV Bayer 04 Leverkusen would not return to the upper leagues until 1962, just one season before the formation of Germany's new professional league, the Bundesliga. The next year saw the club in the Regionalliga West, tier II, where their performances over the next few seasons left them well down the league table.

2. Bundesliga to Bundesliga, UEFA Cup, and DFB-Pokal
SV Bayer 04 Leverkusen made something of a breakthrough in 1968 by winning the division title, but was unable to advance to the playoff round to the first division. The club was relegated again in 1973, but made a quick return to what was now called the 2. Bundesliga after just one season spent in the third division. Four years later, the club secured a place in the Bundesliga to start to play there in the 1979–80 season.

By the mid-1980s, SV Bayer 04 Leverkusen had become established in the upper half of the league table and was well-established there by the end of the decade. It was during this time, in 1984, that the two halves of the club that had parted ways over a half century earlier were re-united as TSV Bayer 04 Leverkusen e.V. The new club took red and white as its colours.

In addition to becoming an established Bundesliga side, the club earned its first honours with a win in the 1988 UEFA Cup. Down 0–3 to Espanyol after the first leg of the final, Bayer Leverkusen drew even in the return match and then won the title on penalty kicks, 3–2.

That same year, long-time Bayer Leverkusen executive Reiner Calmund became the general manager of the club. The decade and a half following this saw club's greatest successes.

After the German reunification in 1990, Reiner Calmund was quick to sign prominent East German players Ulf Kirsten, Andreas Thom and Jens Melzig. The three players would become instant crowd favourites, and make significant contributions to the team. Calmund also established contacts in Brazilian football, befriending Juan Figer, one of Brazil's most powerful player agents. Over the next few years, budding superstars, such as Jorginho and Paulo Sérgio, joined the team, as did Czech star Pavel Hapal. The club also signed charismatic players, such as Bernd Schuster, and Rudi Völler, helping to ensure the team's popularity and growing success.

The club won its next major honour in 1993 with a 1–0 win in the DFB-Pokal final against Hertha BSC second team (amateur squad) on 12 June 1993. In the following season, in a game in which Schuster scored a 45 m "German Goal of the Year" (a goal which was later also named "Goal of the Decade"), Bayer played Eintracht Frankfurt early in the season, and, as both a "tip of the hat" to its own history as well as an attempt to perhaps upset the Frankfurt team, Bayer played in its new third colours, which were old-fashioned red and black stripes, similar jerseys to those Frankfurt generally wore at the time. This proved so popular with the fans that, very shortly thereafter, the team reverted to its "retro" colours of red and black, colours used on all home jerseys ever since.

After a near disaster in 1996 when the club faced a relegation battle, Bayer Leverkusen established itself as a powerful side, offering a technically pleasing offensive style of play under new coach Christoph Daum, who was also helped by the signing of players such as Lúcio, Emerson, Zé Roberto and Michael Ballack. Daum was later to be famously fired for a cocaine scandal that also cost him his ascent to the role of the Germany national team coach.

The Nearly Men
The team earned a series of four second-place finishes from 1997 to 2002. The finishes of 2000 and 2002 were heart-breaking for supporters, as on both occasions the team had the Bundesliga title within its grasp. In 2000, Bayer Leverkusen needed only a draw against SpVgg Unterhaching to win the title, but an own goal by Michael Ballack helped send the team to a crushing 2–0 defeat, while Bayern Munich won the title with a 3–1 victory over Werder Bremen. Two years later, the club surrendered a five-point lead atop the league table by losing two of its last three matches, while Borussia Dortmund swept ahead with three consecutive victories in its final matches. The 2002 season has been dubbed the "Treble Horror", as Bayer Leverkusen were also beaten 4–2 in the DFB-Pokal final by Schalke 04 and lost the UEFA Champions League final 2–1 to Real Madrid, which also led to some of the English-language media dubbing them "Neverkusen". Leverkusen was the first team to reach the final of the Champions League without ever having won a national championship. In addition, five members of the Bayer Leverkusen team were also members of the Germany national team which lost the final of the World Cup of 2002.

Subsequent years
In the 2002 off-season, the team sold midfielders Michael Ballack and Zé Roberto to Bayern Munich. Klaus Toppmöller, who had coached the team during its most successful year, was replaced by the Thomas Hörster. Klaus Augenthaler managed the last two games of the season with a win over his previous club, 1. FC Nürnberg. Bayer Leverkusen finished at a third-place finish and a Champions League place the following year.

That following season's run in the Champions League saw the club open its group stage campaign with a 3–0 win against Real Madrid a result which helped Leverkusen to win the group. Leverkusen, however, was defeated in the first knockout round by eventual champions Liverpool. The club finished sixth during the 2004–05 season to qualify for the next season's UEFA Cup.

Early in 2005, Augenthaler was fired as manager after the club got off to its worst Bundesliga start in over 20 years, with only one win in its first four league matches and a 0–1 home loss to CSKA Sofia in the first leg of its UEFA Cup match-up. Former Germany national team manager Rudi Völler, who had been named sporting director prior to the season, took charge of five matches as caretaker manager. Michael Skibbe, who was Völler's assistant coach with the national team, was named as his successor in October 2005. Skibbe turned Leverkusen's season around, and guided the club to a sixth-place finish in 2006, earning another UEFA Cup place, and then repeated that feat with a fifth place Bundesliga finish in 2007.

The 2007–08 season was not a successful one for Leverkusen despite a good start to the season; five out of the last ten league matches were lost to clubs in the lower half of the table. Michael Skibbe was heavily criticised towards the end of the season after he continuously changed his starting line up. Bayer Leverkusen also lost a lot of its support towards the end of the season: in the 1–2 home loss against Hertha BSC, the Leverkusen fans caused much commotion, with fans chanting for the sacking of Skibbe, while some Ultras, who had seen enough, set fire to their jerseys and threw them onto the field. Michael Skibbe was sacked soon thereafter, leaving the club on 21 May 2008, with club officials stating that his departure was due to the team not qualifying for the following season's UEFA Cup group stage.

The 2008–09 season got off to a great start for Bayer Leverkusen under new manager Bruno Labbadia, who the club had acquired from 2. Bundesliga club SpVgg Greuther Fürth. As the season progressed, however, the team secured no wins against top clubs in the Bundesliga. However, Leverkusen reached the DFB-Pokal final on 30 May 2009 in Berlin, but lost the game 0–1 to Werder Bremen. Leverkusen finished the season in ninth place in the Bundesliga table and Labbadia moved to Hamburger SV in June 2009. Shortly thereafter, Leverkusen presented Jupp Heynckes, who had previously managed Bayern Munich after Jürgen Klinsmann's departure, as its new manager. In the 2010–11 season, Bayer Leverkusen finished as runner-up, thus qualifying for the Champions League for the first time since 2005. However, Heynckes decided not to extend his contract and left Bayer Leverkusen in the 2011 close season to take over at Bayern Munich for a third time. In the 2012–13 and 2015–16 seasons, Leverkusen finished third with coach Sami Hyypiä and Roger Schmidt respectively, but were knocked out in the round of 16 of the Champions League the following season both times. In the 2019–20 UEFA Europa League, Leverkusen reached the quarter-finals for the first time since 2008, but were ultimately knocked out by Inter Milan in a 2–1 loss.

Xabi Alonso era and first ever Bundesliga title
In October 2022, with the club in the relegation zone, Leverkusen appointed Xabi Alonso as head coach in his first senior managerial role; he went on to guide the team to safety and a sixth-placed finish. In 2023–24, Alonso's first full season in charge, Leverkusen achieved significant domestic and European milestones, bolstered by effective squad building and strategic signings including Switzerland captain Granit Xhaka, Jonas Hofmann, Alejandro Grimaldo, and Matej Kovar. By early 2024, they had set a new club record for the longest unbeaten start to a season followed by breaking the Bundesliga record (formerly held by Hamburger SV since the 1982–83 season) for the longest unbeaten run by a club in all competitions with 26 games unbeaten followed by breaking the European record of the European "top 5 leagues" (Bundesliga, Premier League, Primera División, Ligue 1, Serie A) set by Juventus in 2011 and 2012 of 43 cross-competitive compulsory games in a row without defeat. On 14 April 2024, Leverkusen were crowned Bundesliga champions for the first time ever after beating Werder Bremen 5–0, ending Bayern Munich's run of 11 successive league titles. This was the club's first trophy since winning the 1992–93 DFB-Pokal. On 9 May 2024, Leverkusen set a new record for the longest run of matches without a loss in European football history (since the introduction of UEFA club competitions) following a 2–2 draw against Roma in the Europa League; they broke the previous record of 48 set by Benfica between 1963 and 1965. Leverkusen then finished the league season unbeaten, the first club in the Bundesliga to do so. Their unbeaten streak ended in their 52nd game of the season with a hat trick by Ademola Lookman giving them a 3–0 loss to Atalanta in the Europa League final. They won the 2024 DFB-Pokal final by beating 1. FC Kaiserslautern to win the domestic double.

Club culture
In contrast to many other German football clubs, which hold close ties to their working-class roots, Bayer Leverkusen strives for a clean, family-friendly image. The BayArena has the reputation of being one of the most family-friendly football stadiums in Germany. Conversely, Bayer 04 was the first Bundesliga club whose fans identified themselves as Ultras and the city of Leverkusen is one of the old industrial cities of Germany.

Bayer Leverkusen is perceived by some to have an ongoing image problem of a different sort. Although they are a financially healthy club with a stable of strong players, many fans of the traditional clubs denounce Bayer Leverkusen as being a "plastic club" without traditions or a committed fan base, existing solely as a creation of their rich pharmaceutical company sponsor – Bayer AG. As a result, the club and their fans have started to emphasize their industrial origins with pride, calling themselves "Werkself" (Eng. "Company Eleven", "Factory team", "Millhanders") or "Pillendreher" (Eng. "Tablet twisters").

Bayer Leverkusen's corporate origins, however, are far from unique. Other clubs, including PSV, FC Carl Zeiss Jena and Sochaux, share a similar reputation of being works teams. As distinguished from the various Red Bull teams (Salzburg, New York and Leipzig) which have been established or redefined in the recent past primarily for commercial reasons, the formation of Bayer Leverkusen was motivated by the idea of promoting the living conditions of local factory workers early in the 20th century. In view of this tradition, UEFA allows Bayer Leverkusen to use the brand name Bayer in European club competitions while disallowing such naming practices most notably to Red Bull Salzburg.

Charity
In March 2020, Bayer Leverkusen, Borussia Dortmund, Bayern Munich, and RB Leipzig, the four German UEFA Champions League teams for the 2019–20 season, collectively gave €30 million to Bundesliga and to Bundesliga teams that were struggling financially during the COVID-19 pandemic.

League

 * Bundesliga
 * Winners: 2023–24
 * Runners-up: 1996–97, 1998–99, 1999–2000, 2001–02, 2010–11
 * 2. Bundesliga North
 * Winners: 1978–79

Cup

 * DFB-Pokal
 * Winners: 1992–93, 2023–24
 * Runners-up: 2001–02, 2008–09, 2019–20
 * DFL-Supercup
 * Runners-up: 1993

Europe

 * UEFA Champions League
 * Runners-up: 2001–02
 * UEFA Europa League
 * Winners: 1987–88
 * Runners-up: 2023–24

Youth

 * German Under 19 Championship
 * Winners: 1986, 2000, 2007
 * Runners-up: 1995, 2001, 2003, 2010
 * German Under 17 Championship
 * Winners: 1992, 2016
 * Under 19 Bundesliga West
 * Winners: 2007, 2010

Records
Players in bold are active.

Coaching history
• 🇦🇹 Lori Polster (1950)

• Raimond Schwab (1950–51)

• Franz Strehle (1951–53)

• Hans-Josef Kretschmann (1953–56)

• Emil Melcher (1956–57)

• Edmund Conen (1957–59)

• Theo Kirchberg (1959–60)

• Erich Garske (1960–62)

• Fritz Pliska (1962–65)

• Theo Kirchberg (1965–71)

• Gero Bisanz (1971–73)

• Friedhelm Renno (1973–74)

• Manfred Rummel (1974–75)

• Radoslav Momirski (1976–76)

• Willibert Kremer (1 July 1976 – 22 November 1981)

• Gerhard Kentschke (23 November 1981 – 30 June 1982)

• Dettmar Cramer (1 July 1982 – 30 June 1985)

• /🇩🇪 Erich Ribbeck (1 July 1985 – 30 June 1988), (10 April 1995 – 27 April 1996)

• 🇳🇱 Rinus Michels (1 July 1988 – 13 April 1989)

• 🇩🇪 Jürgen Gelsdorf (13 April 1989 – 30 May 1991)

• 🇩🇪 Peter Hermann (31 May 1991 – 30 June 1991)

• 🇩🇪 Reinhard Saftig (1 July 1991 – 4 April 1993)

• Dragoslav Stepanović (4 April 1993 – 7 April 1995)

• 🇩🇪 Peter Hermann (28 April 1996 – 30 June 1996)

• 🇩🇪 Christoph Daum (1 July 1996 – 21 October 2000)

• 🇩🇪 Rudi Völler (21 October 2000–11 November 2000), (16 September 2005 – 9 October 2005)

• 🇩🇪 Berti Vogts (12 November 2000 – 20 May 2001)

• 🇩🇪 Klaus Toppmöller (1 July 2001 – 15 February 2003)

• 🇩🇪 Thomas Hörster (16 February 2003 – 10 May 2003)

• 🇩🇪 Klaus Augenthaler (13 May 2003 – 16 September 2005)

• 🇩🇪 Michael Skibbe (9 October 2005 – 21 May 2008)

• 🇩🇪 Bruno Labbadia (1 July 2008 – 5 June 2009)

• 🇩🇪 Jupp Heynckes (5 June 2009 – 1 July 2011)

• 🇩🇪 Robin Dutt (1 July 2011 – 1 April 2012)

• 🇫🇮 Sami Hyypiä (1 April 2012 – 5 April 2014)

• 🇩🇪 Sascha Lewandowski (5 April 2014 – 1 July 2014)

• 🇩🇪 Roger Schmidt (1 July 2014 – 5 March 2017)

• 🇹🇷 Tayfun Korkut (6 March 2017 – 30 June 2017)

• 🇩🇪 Heiko Herrlich (1 July 2017 – 23 December 2018)

• 🇳🇱 Peter Bosz (23 December 2018 – 23 March 2021)

• 🇩🇪 Hannes Wolf (23 March 2021 – 30 June 2021)

• 🇨🇭 Gerardo Seoane (1 July 2021 – 5 October 2022)

• 🇪🇸 Xabi Alonso (5 October 2022 – present)

title: Bayer 04 Leverkusen


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Sunnyvale (disambiguation)

Sunnyvale, California is a city in Santa Clara County in Silicon Valley.

Sunnyvale may also refer to:

America

 * Sunnyvale, Missouri, a former village in northern Newton County, Missouri, United States
 * Sunnyvale, Texas, a suburb of Dallas, Texas, United States

Australia

 * Sunnyvale, South Australia a locality on Yorke Peninsula

New Zealand

 * Sunnyvale, Auckland, a suburb of Auckland, New Zealand
 * Sunnyvale, Otago, a suburb of Dunedin, New Zealand
 * Sunnyvale Park, a sports venue in Sunnyvale, Dunedin

Arts, entertainment, and media

 * Sunnyvale, the original title of the romantic comedy film Opie Gets Laid
 * Sunnyvale, the fictional suburb from the Australian comedy TV series, Housos
 * Sunnyvale Trailer Park, the fictional home of the characters of Trailer Park Boys
 * Sunnyvale, a fictional town in the Fear Street books by R. L. Stine
 * Sunnyvale, a fictional town for the dolls in the movie Life-Size

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

title: Sunnyvale (disambiguation)


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Deshler

Deshler is the name of some places in the United States of America:

 * Deshler Hotel
 * Deshler, Nebraska
 * Deshler, Ohio
 * Fort Deshler

Deshler may also refer to a surname:
 * Donald D. Deshler, American educator
 * James Deshler, Confederate general in the American Civil War

title: Deshler


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User:Energiza

Industrial Equipment Builder Engineer for 22 years, systems developer for Linux, founder and CEO of Energía SH, founded in 1998. Also Founder and Tech VP of Dos Amigos in 1994, a producer of natural foods, without preservatives including Organic Coffee and natural Chocolate.

title: User:Energiza


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Natural Selection (video game)

Natural Selection is a modification for the video game Half-Life. Its concept is a mixture of the first-person shooter and real-time strategy game genres. The game was created by Charlie "Flayra" Cleveland, who later founded the company Unknown Worlds Entertainment. Natural Selection v1 was first publicly released on Halloween 2002, and is now at version 3.2. Natural Selection 2 was released in late 2012.

The game features two teams: Kharaa (alien species) and Frontiersmen (human space marines). The visible Kharaa "units" are actually simply the spawn of the real Kharaa (aliens) which are microscopic life-forms according to the storyline. The game was, in 2008, one of the ten most played Half-Life modifications in terms of players, according to GameSpy. On January 22, 2014 Unknown Worlds released the source code for download on a GitHub repository under GPLv3.

Gameplay

Natural Selection is a hybrid FPS/RTS that pits Marines against Aliens ("the Kharaa"), played primarily in the first-person perspective, within the confines of large atmospheric maps of spaceships or space stations, which have been invaded by the Kharaa.

There are two game modes in Natural Selection: Classic and Combat.

Classic is the original Natural Selection game, mixing FPS and RTS elements.

Natural Selection was one of the first multiplayer, first-person shooters to incorporate a "Commander", whose view of the battlefield was essentially 2-D, from a strictly top-down perspective. One member of the marine team must enter the "command chair" to lead the team, and thus become the "Commander". From here, he can purchase upgrades, issue movement orders and drop supplies - all from an overhead perspective, as in many RTS games. The commander can also place buildings, although these are inactive until built by players in the field.

The alien team has no defined leader, and so it must communicate and co-operate to ensure different roles are fulfilled as needed. There is only one "Builder Class", the Gorge which can be used to build a multitude of structures. Gorges can choose to build three different "upgrade" chambers: Movement, Defense, and Sensory, each providing three distinct abilities each alien can get. Gorges can also build Offense chambers which damage Marines from a distance, and resource towers. When a resource nozzle is capped, the resources gained are split evenly among all aliens. Each alien can choose how he wants to use his own personal reserve of resource points : building structures (as a gorge), or gestating into higher lifeforms. On death of the lifeform or structure, there is no refund on the resource points spent. Kharaa "Hive Sight" reveals teammates through the walls and structures of the map, this was originally their answer to the commander with his top-down overview of the game. However, in later versions both teams have gained substantial advantage from the addition of a map hotkey. This overlays the whole level, showing teammates and friendly structures; plus enemies and structures currently in view of comrades, and areas under attack.

The aliens start with one active hive, randomly chosen from the 3 hive spots of the map. Active hives heal damaged aliens and respawn dead players. Marines initially spawn in a set location on the map, and after that spawn from Infantry Portals, which can be built in a set radius of the Command Chair. Healing and ammunition can be dropped by the commander in 'packs' (at a cost) or obtained free from an Armoury.

The teams compete for territory, and critically for the resources ('res') it offers. The currency for both sides, resources are obtained by building resource towers to tap the nozzles sited around the map. This is another area where the teams have a key difference - while marines draw on a common pool of resources, each alien accumulates a personal store. This further increases the requirement for teamwork on the alien side, to achieve the right balance of hives, lifeforms, resource towers and chambers.

The game ends when either all the marines or aliens are dead, and have no means to respawn. For the Frontiersmen (marine) side, this entails destroying all alien hives, ensuring no further alien players may respawn, and then hunting the rest down. For the Kharaa (alien) side, winning requires destroying all marine "Infantry Portals", ensuring that they do not respawn, and then eliminating the rest of the marines. Other possibilities exist such as destroying the command chair, or destroying all finished hives, and killing the whole alien team before the remaining hive is fully grown.

Game duration and game balance has been continually addressed by Unknown Worlds Entertainment throughout each of their releases with extensive changelogs describing these issues. In v1, games were slower and often measured in hours. One of the stated aims of v2 was to address this, by introducing a broad range of changes to abilities, structures, etc. In current releases (v3), a typical game lasts 5–15 minutes, but can run over an hour, with both sides vying for control over strategically important Hive Rooms and Resource Nodes.

Combat is the team deathmatch, or beginner mode of NS. It was introduced in NS 3.0 to help new players learn how to play alien lifeforms in an easier environment.

Neither team can build structures, the marines do not have a commander, and aliens have only one hive. Each player has an individual experience meter, increased by killing enemies; dealing damage to the enemy hive/command chair or healing/repairing their own hive/command chair. When the experience meter reaches maximum, the player gains a level and it resets.

There have been various changes to the spawn system in the various betas to try to make matches more interesting and more winnable but also without making it such that a team that suffers a major loss of players can still come back. An admin configurable time limit (default 15 minutes) was introduced in beta 5 which remains in the final version. With the time limit, Marines are usually on the attacking side and Kharaa are on the defending side. Failure of the attacking side to win within the time limit results in a victory for the defending side.

Reception

 * GameSpy hailed Natural Selection as "possibly the most ambitious user-made modification ever brought to fruition. This multiplayer-only mod somehow manages to transform the aging Half-Life into a seemingly brand-new, atmospheric, and surprisingly deep action-strategy game, pitting hive-minded aliens against resourceful space marines," and "Natural Selection's innovations are too numerous to list, but the marine's "commander" mode deserves special mention. Playing as the marine commander, you can order and re-supply other marines in an RTS-style, top-down perspective."
 * FiringSquad : "The way all these details boil down in gameplay is simply amazing, and occasionally fascinating to just watch unfold," and "is probably one of the most creative and well thought out shooters we’ve played in a very long time."
 * Kotaku: "In the end, Natural Selection 2 is perhaps best described as a skeleton, or a scaffold. It's an excellent, stable frame on which to hang cooperation, teamwork, and competition. As a multiplayer game, the experience is exactly as good as a player's allies and enemies are. Strong teams, that join the game and work together to perfect and enhance their skills, could truly be a thing of beauty."

Sequel
Natural Selection 2 is the sequel to Natural Selection. Natural Selection 2 started development using the Source Engine, but their development blog later announced that Unknown Worlds had moved to the Spark engine, an engine they created themselves. Natural Selection 2 has the same basic gameplay as Natural Selection, but with new weapons and abilities. It was released commercially on Steam. According to an interview with the game director, Charlie Cleveland (Flayra), the sequel continues where the original left off and features environments that are dark and denser. Several gameplay modes once available only as community-developed mods (such as Marines vs. Marines) became officially supported. In addition, a major new feature of Natural Selection 2 is dynamic infestation, released in December 2006. The proposed feature would dynamically generate bacterial infestation based on what areas of the map the alien team controlled. The alpha testing of Natural Selection 2 began on 26 July 2010. The game was released on October 30, 2012.

title: Natural Selection (video game)


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Nevada (disambiguation)

Nevada is a state in the southwestern part of the United States.

Nevada may also refer to:

Bosnia and Herzegovina

 * Nevada, Ravno

Serbia

 * Nevada, Kuršumlija

Spain

 * Nevada, Granada

Cities and towns

 * Nevada, Illinois
 * Nevada, Indiana
 * Nevada, Iowa
 * Nevada, Missouri
 * Nevada, Ohio
 * Nevada, Texas

Counties

 * Nevada County, Arkansas
 * Nevada County, California

Natural features

 * Nevada Fall, a waterfall on the Merced River in Yosemite National Park
 * Nevada Gulch, a valley in South Dakota

People

 * Nevada Barr (born 1952), American mystery novelist
 * Nevada Stoody Hayes (1870–1941), American socialite
 * Nevada N. Stranahan (1861 1928), American politician
 * Emma Nevada (1859–1940), American operatic soprano
 * Mignon Nevada (1886–1971), English operatic soprano and daughter of Emma Nevada

Films

 * Nevada (1927 film), a film starring Gary Cooper
 * Nevada (1935 film), a film starring Buster Crabbe
 * Nevada (1944 film), a film starring Robert Mitchum
 * Nevada (1997 film), a film directed by Gary Tieche

Literature

 * Nevada (Binnie novel), a 2013 novel by Imogen Binnie
 * Nevada (Grey novel), a 1927 novel by Zane Grey

Groups

 * Nevada (musician), an American musician, DJ, producer, and songwriter
 * Nevada (British band), a British folk/progressive rock band
 * Nevada (Portuguese band), a Portuguese duo

Songs

 * "Nevada" (song), by YoungBoy Never Broke Again
 * "Nevada", song by Riders in the Sky from Prairie Serenade
 * "Nevada", song by Vicetone featuring Cozi Zuehlsdorff
 * "Nevada", song by John Linnell from State Songs

Other uses in arts, entertainment, and media

 * Nevada (comics), a comic series published by DC under its Vertigo imprint
 * Supershow Nevada, a Bulgarian TV game show

Other uses

 * Nevada, the development code name for the Solaris release following Solaris 10
 * Nevada Solar One, a solar power plant in Nevada, United States
 * Renault Nevada, a model of automobile
 * University of Nevada, Reno
 * Nevada Wolf Pack, the school's athletic program
 * USS Nevada, a United States naval vessel
 * Nevada-tan, a nickname given to the perpetrator of the Sasebo Slashing

title: Nevada (disambiguation)


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Rejection sampling

In numerical analysis and computational statistics, rejection sampling is a basic technique used to generate observations from a distribution. It is also commonly called the acceptance-rejection method or "accept-reject algorithm" and is a type of exact simulation method. The method works for any distribution in $$\mathbb{R}^m$$ with a density.

Rejection sampling is based on the observation that to sample a random variable in one dimension, one can perform a uniformly random sampling of the two-dimensional Cartesian graph, and keep the samples in the region under the graph of its density function. Note that this property can be extended to N-dimension functions.

Description
To visualize the motivation behind rejection sampling, imagine graphing the probability density function (PDF) of a random variable onto a large rectangular board and throwing darts at it. Assume that the darts are uniformly distributed around the board. Now remove all of the darts that are outside the area under the curve. The remaining darts will be distributed uniformly within the area under the curve, and the $$x$$‑positions of these darts will be distributed according to the random variable's density. This is because there is the most room for the darts to land where the curve is highest and thus the probability density is greatest.

The visualization just described is equivalent to a particular form of rejection sampling where the "proposal distribution" is uniform. Hence its graph is a rectangle. The general form of rejection sampling assumes that the board is not necessarily rectangular but is shaped according to the density of some proposal distribution (not necessarily normalized to $$1$$) that we know how to sample from (for example, using inversion sampling). Its shape must be at least as high at every point as the distribution we want to sample from, so that the former completely encloses the latter. Otherwise, there would be parts of the curved area we want to sample from that could never be reached.

Rejection sampling works as follows:

 * 1) Sample a point on the $$x$$‑axis from the proposal distribution.
 * 2) Draw a vertical line at this $$x$$‑position, up to the maximum y-value of the probability density function of the proposal distribution.
 * 3) Sample uniformly along this line from 0 to the maximum of the probability density function. If the sampled value is greater than the value of the desired distribution at this vertical line, reject the $$x$$‑value and return to step 1; else the $$x$$‑value is a sample from the desired distribution.

This algorithm can be used to sample from the area under any curve, regardless of whether the function integrates to 1. In fact, scaling a function by a constant has no effect on the sampled $x$‑positions. Thus, the algorithm can be used to sample from a distribution whose normalizing constant is unknown, which is common in computational statistics.

Theory
The rejection sampling method generates sampling values from a target distribution $$X$$ with an arbitrary probability density function $$f(x)$$ by using a proposal distribution $$Y$$ with probability density $$g(x)$$. The idea is that one can generate a sample value from $$X$$ by instead sampling from $$Y$$ and accepting the sample from $$Y$$ with probability $$f(x)/(M g(x))$$, repeating the draws from $$Y$$ until a value is accepted. $$M$$ here is a constant, finite bound on the likelihood ratio $$f(x)/g(x)$$, satisfying $$M < \infty$$ over the support of $$X$$; in other words, M must satisfy $$f(x) \leq M g(x)$$ for all values of $$x$$. Note that this requires that the support of $$Y$$ must include the support of $$X$$—in other words, $$g(x) > 0$$ whenever $$f(x) > 0$$.

The validation of this method is the envelope principle: when simulating the pair $(x,v=u\cdot Mg(x))$, one produces a uniform simulation over the subgraph of $Mg(x)$. Accepting only pairs such that $u<f(x)/(Mg(x))$ then produces pairs $$(x,v)$$ uniformly distributed over the subgraph of $$f(x)$$ and thus, marginally, a simulation from $$f(x).$$

This means that, with enough replicates, the algorithm generates a sample from the desired distribution $$f(x)$$. There are a number of extensions to this algorithm, such as the Metropolis algorithm.

This method relates to the general field of Monte Carlo techniques, including Markov chain Monte Carlo algorithms that also use a proxy distribution to achieve simulation from the target distribution $$f(x)$$. It forms the basis for algorithms such as the Metropolis algorithm.

The unconditional acceptance probability is the proportion of proposed samples which are accepted, which is $$ \begin{align} \mathbb{P}\left(U\le\frac{f(Y)}{M g(Y)}\right) &= \operatorname{E}\mathbf{1}_{\left[U\le\frac{f(Y)}{M g(Y)}\right]}\\[6pt] &= E\left[\operatorname{E}[\mathbf{1}_{\left[U\le\frac{f(Y)}{M g(Y)}\right]}| Y]\right] & (\text{by tower property } ) \\[6pt] &= \operatorname{E}\left[\mathbb{P}\left(U\le \frac{f(Y)}{Mg(Y)} \biggr| Y\right) \right]\\[6pt] &= E\left[\frac{f(Y)}{M g(Y)}\right] & (\text{because } \Pr(U \leq u) = u, \text{when } U \text{ is uniform on } (0,1)) \\[6pt] &=\int\limits_{y: g(y) > 0} \frac{f(y)}{M g(y)} g(y) \, dy\\ [6pt] &= \frac{1}{M}\int\limits_{y: g(y) > 0} f(y) \, dy \\[6pt] &= \frac{1}{M} & (\text{since support of } Y \text{ includes support of } X) \end{align} $$where $$U\sim \mathrm{Unif}(0,1)$$, and the value of $$y$$ each time is generated under the density function $$g(\cdot)$$ of the proposal distribution $$Y$$.

The number of samples required from $$Y$$ to obtain an accepted value thus follows a geometric distribution with probability $$1/M$$, which has mean $$M$$. Intuitively, $$M$$ is the expected number of the iterations that are needed, as a measure of the computational complexity of the algorithm.

Rewrite the above equation, $$M=\frac{1}{\mathbb{P}\left(U\le\frac{f(Y)}{M g(Y)}\right)}$$ Note that $1 \le M<\infty$, due to the above formula, where $\mathbb{P}\left(U\le\frac{f(Y)}{M g(Y)}\right)$ is a probability which can only take values in the interval $$[0,1]$$. When $$M$$ is chosen closer to one, the unconditional acceptance probability is higher the less that ratio varies, since $$M$$ is the upper bound for the likelihood ratio $f(x)/g(x)$. In practice, a value of $$M$$ closer to 1 is preferred as it implies fewer rejected samples, on average, and thus fewer iterations of the algorithm. In this sense, one prefers to have $$M$$ as small as possible (while still satisfying $$f(x) \leq M g(x)$$, which suggests that $$g(x)$$ should generally resemble $$f(x)$$ in some way. Note, however, that $$M$$ cannot be equal to 1: such would imply that $$f(x)=g(x)$$, i.e. that the target and proposal distributions are actually the same distribution.

Rejection sampling is most often used in cases where the form of $$f(x)$$ makes sampling difficult. A single iteration of the rejection algorithm requires sampling from the proposal distribution, drawing from a uniform distribution, and evaluating the $$f(x)/(M g(x))$$ expression. Rejection sampling is thus more efficient than some other method whenever M times the cost of these operations—which is the expected cost of obtaining a sample with rejection sampling—is lower than the cost of obtaining a sample using the other method.

Algorithm
The algorithm, which was used by John von Neumann and dates back to Buffon and his needle, obtains a sample from distribution $$X$$ with density $$f$$ using samples from distribution $$Y$$ with density $$g$$ as follows:
 * Obtain a sample $$y$$ from distribution $$Y$$ and a sample $$u$$ from $$\mathrm{Unif}(0,1)$$ (the uniform distribution over the unit interval).
 * Check if $u<f(y)/Mg(y)$.
 * If this holds, accept $$y$$ as a sample drawn from $$f$$;
 * if not, reject the value of $$y$$ and return to the sampling step.

The algorithm will take an average of $$M$$ iterations to obtain a sample.

Advantages over sampling using naive methods
Rejection sampling can be far more efficient compared with the naive methods in some situations. For example, given a problem as sampling $X\sim F(\cdot)$ conditionally on $$X$$ given the set $$A$$, i.e., $X|X\in A$, sometimes $X$  can be easily simulated, using the naive methods (e.g. by inverse transform sampling): The problem is this sampling can be difficult and inefficient, if $\mathbb{P}(X\in A)\approx 0$. The expected number of iterations would be $$\frac{1}{\mathbb{P}(X\in A)}$$, which could be close to infinity. Moreover, even when you apply the Rejection sampling method, it is always hard to optimize the bound $$M$$ for the likelihood ratio. More often than not, $$M$$ is large and the rejection rate is high, the algorithm can be very inefficient. The Natural Exponential Family (if it exists), also known as exponential tilting, provides a class of proposal distributions that can lower the computation complexity, the value of $$M$$ and speed up the computations (see examples: working with Natural Exponential Families).
 * Sample $X\sim F(\cdot)$ independently, and accept those satisfying $$\{n\ge 1: X_n\in A\}$$
 * Output: $$\{X_1,X_2,...,X_N:X_i\in A, i=1,...,N\}$$ (see also truncation (statistics))

Rejection sampling using exponential tilting
Given a random variable $$X\sim F(\cdot)$$, $$F(x)=\mathbb{P}(X\le x)$$ is the target distribution. Assume for simplicity, the density function can be explicitly written as $$f(x)$$. Choose the proposal as

$$\begin{align} F_\theta (x)&=\mathbb{E}\left[\exp(\theta X-\psi (\theta))\mathbb{I}(X\le x)\right]\\ &=\int^x_{-\infty}e^{\theta y-\psi(\theta)}f(y)dy\\ g_\theta(x)&=F'_\theta(x)=e^{\theta x-\psi(\theta)}f(x) \end{align}$$

where $$\psi(\theta) = \log\left(\mathbb{E}\exp(\theta X)\right)$$ and $$\Theta = \{\theta :\psi(\theta)<\infty\}$$. Clearly, $$\{F_\theta (\cdot)\}_{\theta \in \Theta}$$, is from a natural exponential family. Moreover, the likelihood ratio is

$$Z(x)=\frac{f(x)}{g_\theta(x)}=\frac{f(x)}{e^{\theta x-\psi(\theta)}f(x)}=e^{-\theta x+\psi(\theta)}$$

Note that $$\psi (\theta)<\infty$$ implies that it is indeed a cumulant-generation function, that is,

 * $$\psi(\theta)=\log\mathbb{E}{\exp(tX)}|_{t=\theta}=\log M_X(t)|_{t=\theta}$$.

It is easy to derive the cumulant-generation function of the proposal and therefore the proposal's cumulants.

\begin{align} \psi_\theta(\eta)&=\log\left(\mathbb{E}_\theta\exp(\eta X)\right)=\psi(\theta+\eta)-\psi(\theta)<\infty\\ \mathbb{E}_\theta(X)&= \left.\frac{\partial \psi_\theta(\eta)}{\partial\eta}\right|_{\eta=0}\\ \mathrm{Var}_\theta(X)&= \left.\frac{\partial^2 \psi_\theta(\eta)}{\partial^2\eta}\right|_{\eta=0} \end{align} $$

As a simple example, suppose under $$F(\cdot)$$, $$X \sim \mathrm{N}(\mu, \sigma^2)$$, with $\psi(\theta)= \mu\theta+\frac{\sigma^2\theta^2}{2}$. The goal is to sample $$X|X\in \left[b,\infty\right]$$, where $$b>\mu$$. The analysis goes as follows:

 * Choose the form of the proposal distribution $$F_\theta(\cdot)$$, with cumulant-generating function as
 * $\psi_\theta(\eta) = \psi (\theta+\eta) - \psi(\theta) = (\mu+\theta\sigma^2)\eta + \frac{\sigma^2\eta^2}{2}$ ,

 * which further implies it is a normal distribution $$\mathrm{N}(\mu+\theta\sigma^2, \sigma^2)$$.

 * Decide the well chosen $$\theta^*$$ for the proposal distribution. In this setup, the intuitive way to choose $$\theta^*$$ is to set
 * $$\mathbb{E}_{\theta}(X)=\mu+\theta\sigma^2=b$$,
 * that is $$\theta^*=\frac{b-\mu}{\sigma^2}.$$ The proposal distribution is thus $$g_{\theta^*}(x) = \mathrm{N}(b, \sigma^2)$$.

 * Explicitly write out the target, the proposal and the likelihood ratio

\begin{align} f_{X|X\ge b}(x)&=\frac{f(x)\mathbb{I}(x\ge b)}{\mathbb{P}(X\ge b)}\\ g_{\theta^*}(x)&=f(x)\exp(\theta^* x-\psi(\theta^*))\\ Z(x)&=\frac{f_{X|X\ge b}(x)}{g_{\theta^*}(x)}=\frac{\exp(-\theta^* x+\psi(\theta^*))\mathbb{I}(x\ge b)}{\mathbb{P}(X\ge b)} \end{align} $$

 * Derive the bound $$M$$ for the likelihood ratio $$Z(x)$$, which is a decreasing function for $$x \in [b, \infty]$$, therefore

 * $$M=Z(b)=\frac{\exp(-\theta^*b+\psi(\theta^*))}{\mathbb{P}(X\ge b)} = \frac{\exp\left(-\frac{(b-\mu)^2}{2\sigma^2}\right)}{\mathbb{P}(X\ge b)} = \frac{\exp\left(-\frac{(b-\mu)^2}{2\sigma^2}\right)}{\mathbb{P}\left(\mathrm{N}(0,1)\ge\frac{b-\mu}{\sigma}\right)}$$

 * Rejection sampling criterion: for $$U\sim \mathrm{Unif}(0,1)$$, if

 * $$U\le \frac{Z(x)}{M}=e^{-\theta^*(x-b)}\mathbb{I}(x\ge b)$$

holds, accept the value of $$X$$; if not, continue sampling new $X\sim_{i.i.d.}\mathrm{N}(\mu+\theta^*\sigma^2,\sigma^2)$ and new $U\sim \mathrm{Unif}(0,1)$  until acceptance.

For the above example, as the measurement of the efficiency, the expected number of the iterations the natural exponential family based rejection sampling method is of order $$b$$, that is $$M(b)=O(b)$$, while under the naive method, the expected number of the iterations is $\frac{1}{\mathbb{P}(X\ge b)}=O(b\cdot e^{\frac{(b-\mu)^2}{2\sigma^2}})$, which is far more inefficient.

In general, exponential tilting a parametric class of proposal distribution, solves the optimization problems conveniently, with its useful properties that directly characterize the distribution of the proposal. For this type of problem, to simulate $$X$$ conditionally on $$X\in A$$, among the class of simple distributions, the trick is to use natural exponential family, which helps to gain some control over the complexity and considerably speed up the computation. Indeed, there are deep mathematical reasons for using natural exponential family.

Drawbacks
Rejection sampling requires knowing the target distribution (specifically, ability to evaluate target PDF at any point).

Rejection sampling can lead to a lot of unwanted samples being taken if the function being sampled is highly concentrated in a certain region, for example a function that has a spike at some location. For many distributions, this problem can be solved using an adaptive extension (see adaptive rejection sampling), or with an appropriate change of variables with the method of the ratio of uniforms. In addition, as the dimensions of the problem get larger, the ratio of the embedded volume to the "corners" of the embedding volume tends towards zero, thus a lot of rejections can take place before a useful sample is generated, thus making the algorithm inefficient and impractical. See curse of dimensionality. In high dimensions, it is necessary to use a different approach, typically a Markov chain Monte Carlo method such as Metropolis sampling or Gibbs sampling. (However, Gibbs sampling, which breaks down a multi-dimensional sampling problem into a series of low-dimensional samples, may use rejection sampling as one of its steps.)

Adaptive rejection sampling
For many distributions, finding a proposal distribution that includes the given distribution without a lot of wasted space is difficult. An extension of rejection sampling that can be used to overcome this difficulty and efficiently sample from a wide variety of distributions (provided that they have log-concave density functions, which is in fact the case for most of the common distributions—even those whose density functions are not concave themselves) is known as adaptive rejection sampling (ARS).

There are three basic ideas to this technique as ultimately introduced by Gilks in 1992:

 * 1) If it helps, define your envelope distribution in log space (e.g. log-probability or log-density) instead. That is, work with $$ h\left(x\right) = \log g\left(x\right)$$ instead of $$ g\left(x\right)$$ directly.
 * 2) * Often, distributions that have algebraically messy density functions have reasonably simpler log density functions (i.e. when $$f\left( x \right) $$ is messy, $$ \log f\left(x\right)$$ may be easier to work with or, at least, closer to piecewise linear).
 * 3) Instead of a single uniform envelope density function, use a piecewise linear density function as your envelope instead.
 * 4) * Each time you have to reject a sample, you can use the value of $$ f \left(x \right) $$ that you evaluated, to improve the piecewise approximation $$ h \left(x \right) $$. This therefore reduces the chance that your next attempt will be rejected. Asymptotically, the probability of needing to reject your sample should converge to zero, and in practice, often very rapidly.
 * 5) * As proposed, any time we choose a point that is rejected, we tighten the envelope with another line segment that is tangent to the curve at the point with the same x-coordinate as the chosen point.
 * 6) * A piecewise linear model of the proposal log distribution results in a set of piecewise exponential distributions (i.e. segments of one or more exponential distributions, attached end to end). Exponential distributions are well behaved and well understood. The logarithm of an exponential distribution is a straight line, and hence this method essentially involves enclosing the logarithm of the density in a series of line segments. This is the source of the log-concave restriction: if a distribution is log-concave, then its logarithm is concave (shaped like an upside-down U), meaning that a line segment tangent to the curve will always pass over the curve.
 * 7) * If not working in log space, a piecewise linear density function can also be sampled via triangle distributions
 * 8) We can take even further advantage of the (log) concavity requirement, to potentially avoid the cost of evaluating $$f \left( x \right)$$ when your sample is accepted.
 * 9) * Just like we can construct a piecewise linear upper bound (the "envelope" function) using the values of $$ h \left(x \right) $$ that we had to evaluate in the current chain of rejections, we can also construct a piecewise linear lower bound (the "squeezing" function) using these values as well.
 * 10) * Before evaluating (the potentially expensive) $$f \left( x \right)$$ to see if your sample will be accepted, we may already know if it will be accepted by comparing against the (ideally cheaper) $$ g_l \left( x \right) $$ (or $$h_l \left( x \right)$$ in this case) squeezing function that have available.
 * 11) * This squeezing step is optional, even when suggested by Gilks. At best it saves you from only one extra evaluation of your (messy and/or expensive) target density. However, presumably for particularly expensive density functions (and assuming the rapid convergence of the rejection rate toward zero) this can make a sizable difference in ultimate runtime.

The method essentially involves successively determining an envelope of straight-line segments that approximates the logarithm better and better while still remaining above the curve, starting with a fixed number of segments (possibly just a single tangent line). Sampling from a truncated exponential random variable is straightforward. Just take the log of a uniform random variable (with appropriate interval and corresponding truncation).

Unfortunately, ARS can only be applied for sampling from log-concave target densities. For this reason, several extensions of ARS have been proposed in literature for tackling non-log-concave target distributions. Furthermore, different combinations of ARS and the Metropolis-Hastings method have been designed in order to obtain a universal sampler that builds a self-tuning proposal densities (i.e., a proposal automatically constructed and adapted to the target). This class of methods are often called as Adaptive Rejection Metropolis Sampling (ARMS) algorithms. The resulting adaptive techniques can be always applied but the generated samples are correlated in this case (although the correlation vanishes quickly to zero as the number of iterations grows).

title: Rejection sampling


text:
Władysław Franciszek Jabłonowski

Władysław Franciszek Jabłonowski (25 October 1769 – 29 September 1802) was a Polish military officer who served in the French Revolutionary Army during the Napoleonic Wars. He is the first known Polish general of African descent.

After joined the French army, he died of yellow fever in 1802 in Saint-Domingue, after being sent there as part of the Saint-Domingue expedition, which saw Polish troops fighting alongside the French to restore slavery in the colony. After the French and Polish suffered heavily from yellow fever, they withdrew their surviving forces from Saint-Domingue.

Some of Polish soldiers sent to Saint-Domingue deserted and joined the Haitian rebels in their quest for independence, and about 400 settled on the island after the war. They were granted full citizenship by Jean-Jacques Dessalines, who led the country.

Early life
Of mixed ancestry, Władysław was the illegitimate child of Princess Maria Franciszka Dealire, who was born in Britain and married into the Polish aristocracy, and an unidentified man of African descent. He acquired the nickname "Murzynek". Dealire's husband, Polish nobleman Konstanty Jabłonowski, accepted the boy as his son and gave him his family name, so he was considered Polish.

In 25 February 1783, Jabłonowski as a youth was admitted to the French military academy at Paris École Militaire. There he was a schoolmate of Napoleon Bonaparte and Louis Nicolas D'avout. In a climate of bullying, he was subjected to racist taunts, including from Napoleon. Upon graduation on 20 February 1786, he joined the Régiment de Royal-Allemand with the rank of second lieutenant. He achieved the rank of lieutenant colonel within 8 years.

Military career
In 1794 Jabłonowski fought in Tadeusz Kościuszko's uprising against Tsarist Russia. He participated in battles of Szczekociny, Warsaw, Maciejowice, and at Praga. A member of the Polish insurrectionist group Centralizacja Lwowska, he was sent to the Ottoman Empire to gain the support of the Ottoman and French governments for the creation a Polish army to antagonize Russia. Another opportunity for this later appeared with the French fighting in Italy.

He was appointed brigade chief in the French army on 4 January 1798. In 1799, he was under the command of General Jan Henryk Dąbrowski. In 1799 he was made General of Brigade of the Polish legions (the Dąbrowski Legion). He became provisional brigade general in the Army of Italy on 20 May 1800 after being nominated by General André Massena. However, his French superiors would not approve this rank. He distinguished himself in the battle of Bosco and Pasturana and commanded a legion in the Alps.

On July 24, 1801, he was again nominated to the rank of brigade general in the Polish legions by General Joachim Murat, an appointment that was confirmed on 21 December 1801 by the First Consul. In 1801, he took strategic command of the 113th Infantry Demi-Brigade, which was formed from the Legia Naddunajska. He enlisted in the colonial forces to serve in Louisiana or Saint-Domingue. He was sent at his own request to Saint-Domingue in May 1802 to assist French forces (before Napoleon recruited more of the Polish legions to assist his French forces), and was accompanied by his common-law wife Anne Penot. There he worked to put down the Haitian Revolution being waged by formerly enslaved Africans. Jabłonowski died from yellow fever on 29 September 1802 in Jérémie, three weeks after more Legionnaires landed but before he had seen any.

The disease caused many deaths among both French and Polish forces, killing more than those who died because of warfare. Eventually some 400 of the surviving Polish Legions (who started with 5200 soldiers) abandoned the French and joined the slaves in their fight for freedom. They settled in what became Haiti, where their descendants are known as Polish Haitians.

In Polish culture
Jabłonowski is mentioned in Adam Mickiewicz's notable epic poem Pan Tadeusz, in the context of a veteran of the Polish legions recounting what he had seen:
 * how Jabłonowski had reached the land where the pepper grows
 * ''and where sugar is produced, and where in eternal spring'
 * bloom fragrant woods: with the legion of the Danube there
 * the Polish general smites the Negroes, but sighs for his native soil

title: Władysław Franciszek Jabłonowski


text:
Cancer Cell (journal)

Cancer Cell is a peer-reviewed scientific journal scientific journal that publishes articles that provide major advances in cancer research and oncology. The journal considers manuscripts that answer important questions relevant to naturally occurring cancers. Areas covered include basic cancer biology, therapeutic development, translational research, cancer model development, multi-omics and computational biology. Cancer Cell is also interested in publishing clinical investigations, in particular those that lead to establishing new paradigms in the treatment, diagnosis, or prevention of cancers; those that provide important insights into cancer biology beyond what has been revealed by preclinical studies; and those that are mechanism-based proof-of-principle clinical studies.

Cancer Cell is internationally regarded as one of the top cancer research and oncology journals. According to the Journal Citation Reports, the 2022 impact factor of the journal is 50.3. It is part of the Cell Press portfolio, which is owned by Elsevier. Issues are published monthly in print and online versions. All content becomes available for free one year after publication. In January 2021, Cancer Cell became a Transformative Journal as part of Elsevier's efforts to increase Open Access content.

Cancer Cell publishes Research, Review, and Perspective articles, in addition to opinion pieces such as Letters, Commentaries, Previews, Spotlight, and Voices. The journal often publishes a yearly special issue with a compilation of review articles around a common topic. In the Cancer Cell website, articles can be browsed by Issue, Collection, early online access ("Online Now"), or annual "Best of Cancer Cell".

History
Cancer Cell was launched in 2002 with the publication of its inaugural issue on February 1, 2002. In an Editorial published in this first issue, Editor-in-Chief Mariana Resnicoff introduced Cancer Cell as "An exciting forum for cancer research". Following in the footsteps of the Cell Press flagship journal Cell, the goal of Cancer Cell was to publish cutting-edge findings in cancer research and to provide great author service by having a team of dedicated scientific editors that hold the highest standards of excellence, editorial consistency, and author service. Li-Kuo Su became Editor-in-Chief in 2003 and is recognized for promoting the journal's growth and establishing it as one of the most reputable cancer research journals. He retired from his editorial activities in 2019. Steve Mao became Cancer Cell Editor-in-Chief in 2020 after serving as senior editor at Cell and Science.

Changing for a better future editorial
In the April 2020 special issue of Cancer Cell, Steve Mao published the editorial "Changing for a Better Future" delineating his views for the journal. This Editorial reiterated the journal's commitment to scientific excellence and introduced key changes. While Cancer Cell is traditionally considered a journal interested in strong mechanistic studies, the Editorial highlights its interest in translational and clinical studies, as well in discovery papers that scientifically sound and conceptually bold. In line with the Cell Press rebranding strategy launched in 2020, Steve Mao vowed to continue publishing "Science that Inspires". The Editorial also states the editors' commitment to increase communication with its scientific community and authors, as well as to work with authors to improve the revision process of manuscripts under consideration.

Diversity and inclusion efforts
In 2020, Cancer Cell made a number of changes to increase diversity and inclusion of underrepresented minorities in cancer research and oncology. In June 2020, the journal announced the new members of its advisory board (previously editorial board), which includes a higher representation of female scientists (>40%) and physicians (~50%) and higher geographical and ethnic diversity. That same month, the Cell Editorial Team published the editorial "Science has a racism problem", acknowledging the underrepresentation of Black scientists in STEM in the wake of the murders of George Floyd, Breonna Taylor, and Ahmaud Arbery. These events, in conjunction with the anti-immigration policies of the Trump administration in the U.S., led to a Summer of social reckoning in the scientific community. Cancer Cell joined the cancer research and oncology communities and their call for action and change by dedicating its September 2020 issue to the importance of diversity and immigration in cancer research in the U.S. and publishing a series of opinion pieces by scientists and clinicians that represent several minorities (immigrants, Black, Latinx, LGBTQ) and their allies. The journal has also published several articles that address ethnic health disparities and aim to increase the representation of non-Caucasian populations in cancer research and oncology.

As a part of Cell Press, Cancer Cell has supported Black in Cancer and the #BlackInCancerWeek campaign on Twitter. The journal also participated in the creation of the "Rising Black Scientists Award", a cash award granted to one undergraduate student and one graduate student or postdoctoral trainee "meant to break down barriers and create opportunities by providing funds to support professional development".

In January 2021, Cell Press introduced the Inclusion and Diversity Statement, in which authors may choose to disclose information related to how they are increasing their effort to promote genetic diversity in their study subjects, participation of minorities, and balancing gender representation, to name a few.

Editorial process
Articles submitted to Cancer Cell are assigned to an in-house, professional scientific editor (handling editor). The handling editor reads the full manuscript, evaluates the relevant literature, and leads the editorial discussion with the rest of the editorial team to make a decision on how to proceed with the paper. Advisory Board Members and other experts may be confidentially contacted at this point to ask for their professional opinion to facilitate the initial decision making. Initial decisions are usually made within 5–7 days. If a paper is selected for peer review, the handling editor invites 3-4 reviewers whose areas of expertise cover different aspects of the paper. Reviewers submit their comments and recommendation within two weeks. After review, the handling editor evaluates the reviewers' comments, discusses them with the editorial team, and makes a decision on the paper. If a manuscript is invited to revise, the handling editor may communicate with the authors to agree on the revision plan and deadlines for resubmission. Once the revised manuscript is submitted, the handling editor may request additional feedback from the reviewers. Cancer Cell typically only allows for one round of major revisions for its articles. If the article is deemed suitable for publication in Cancer Cell, the handling editor will work with the authors to ensure the paper complies with the journal's format and guidelines, and that it communicates results clearly and accurately. Only after all the requirements are met will a paper be formally accepted for publication.

Other Scientific activities
Cancer Cell editors are involved in a diverse array of activities at Cell Press besides manuscript handling, such as organizing Cell Symposia, Webinars, and Cell Press Selections. Editors also attend national and international cancer research and oncology meetings, conduct site visits, and participate in career panels to interact with the scientific and medical community, as well as with students and trainees.

title: Cancer Cell (journal)


text:
Cobb

Cobb may refer to:

People

 * Cobb (surname), a list of people and fictional characters with the surname Cobb
 * Cobb Rooney (1900–1973), American professional football running back

New Zealand

 * Cobb River
 * Cobb Reservoir
 * Cobb Power Station

United Kingdom

 * The Cobb, the harbour wall in Lyme Regis, England

United States

 * Cobb, California, a census designated place
 * Cobb, former name of Pine Grove, Lake County, California
 * Cobb Mountain, California
 * Cobb County, Georgia
 * Cobb, Georgia, an unincorporated community
 * Cobb, Indiana, an unincorporated town
 * Cobb, Kentucky, an unincorporated community
 * Cobb, Oklahoma, an unincorporated community
 * Cobb Peak (Idaho)
 * Cobb Peak, Tooele County, Utah
 * Cobb River (Minnesota)
 * Cobb, St. Clair County, Missouri, an unincorporated community
 * Cobb, Stoddard County, Missouri, an unincorporated community
 * Cobb, Texas, an unincorporated community
 * Mount Cobb, Washington County, Vermont, Vermont
 * Cobb Town, Wisconsin, an unincorporated community
 * Cobb, West Virginia, an unincorporated community
 * Cobb, Wisconsin, a village
 * Fort Cobb, a US Army fort established in 1859 in what is now Caddo County, Oklahoma

Elsewhere

 * Mount Cobb (British Columbia), Canada
 * Cobb Seamount, an underwater volcano 500 km (310 mi) west of Gray's Harbor, Washington
 * Cobb hotspot, a volcanic hotspot off the Oregon/Washington coast

Buildings

 * Cobb Building (Wagoner, Oklahoma), on the National Register of Historic Places in Oklahoma
 * Cobb Building (Seattle, Washington), on the National Register of Historic Places listings in Washington state
 * Cobb House (disambiguation), various houses on the National Register of Historic Places
 * Cobb Stadium, a multi-purpose stadium on the University of Miami campus in Coral Gables, Florida

Roads

 * Cobb Highway, New South Wales, Australia
 * Cobb Parkway, Cobb County, Georgia

Other uses

 * Cob (material), or cobb, a building material for making walls using compacted clay, sand and straw
 * Cobb (film), a 1994 film about Ty Cobb
 * Cobb (soundtrack)
 * Cobb, today Cobb-Vantress, a brand of commercial broiler chicken
 * CobB, a bacterial protein
 * Cobb & Co, the name of many Australian coaching businesses
 * Cobb & Co. (New Zealand), a 19th-century operator of a fleet of stagecoaches
 * Cobb baronets, an extinct title in the Baronetage of England
 * Cobb Divinity School, a former graduate school
 * Cobb Field, a baseball park in Billings, Montana
 * Cobb lettuce or Boston lettuce, a variety of lettuce
 * Cobb salad, a chopped salad named for Robert Howard Cobb
 * Cobb Surgical elevator, an orthopedic surgical instrument used to scrape bone
 * USCGC Cobb (WPG-181), a US Coast Guard cutter

title: Cobb


text:
Bardylis

Bardylis (also Bardyllis ; Βάρδυλις; c. 448 – c. 358 BC) was an Illyrian king, and the founder of the first attested Illyrian dynasty. During his reign, Bardylis aimed to make Illyria a regional power interfering with Macedon. He united many southern Illyrian tribes under his realm and defeated the Macedonians and Molossians several times, expanding his dominion over Upper Macedonia, including Lynkestis, and ruling over Macedon through a puppet king. Before the Rise of Macedon Illyrians were the dominant power in the region. Bardylis also led raids against Epirus, but his soldiers were eventually expelled from the region.

Most scholars hold that the Illyrian kingdom that was established by Bardylis was centered along Lake Ohrid and east to the Prespa Lakes, which was called Dassaretis later in Roman times, located on the border between Macedon and Epirus. Illyrians, in particular under Bardylis' leadership, held a remarkable role in the development of the Argead Macedonian military. Philip II and Alexander the Great spent at least some of their youth age at Illyrian courts, getting well familiar with Illyrian customs, habits and specially military strategies and techniques.

According to ancient sources, Bardylis lived to over 90 years. According to these sources, Bardylis lived a long time and was at an advanced age when he faced Philip II of Macedon in 358 BC and at whose hands he died after Philip rejected his offer of peace based on Bardylis retaining conquered lands. Bardylis' son Cleitus revolted with the help of Taulantian king Glaukias against Alexander the Great in the siege of Pelium undertaken in 335 BC.

Name

The name Bardylis/Bardulis contains the same root as the Albanian adjective i bardhë "white", ultimately from Proto-Indo-European *bʰór(h₁)ǵos < *bʰreh₁ǵ- ("to gleam, shine"). The same root is found in the Messapic names Barzidihi, Barduli and Barletta.

The name Bardylis/Bardulis, spelled in Ancient Greek as ΒΑΡΔΥΛΙΣ, is among those Illyrian names that featured Albanoid affricates, which were not familiar to Ancient Greek and Roman authors, who transcribed them with alternating spellings using dentals and sibilants, and in particular they used Δ/D for the Illyrian king's name.

Reign
Bardylis was born around the year 448 BC. Bardylis became king despite his humble roots. A charcoal-burner and coal miner, he gained power by force and enjoyed the sympathy of the Illyrian warriors because he divided the spoils of war fairly and impartially. Bardylis did not succeed Sirras, but rather the previous Illyrian king who had entered into a peace treaty with Amyntas II over the control of Lynkestis. Bardylis succeeded in bringing the various Illyrian tribes together and soon made his kingdom a formidable power in the Balkans, resulting in a change in relations with Macedon.

No ancient sources identify Bardylis with any tribe or regime other than Illyrians and nothing is recorded about the center of his power, except that Philip's victory against him in 358 BC gained control of Lyncestis and the territory as far as Lake Ohrid. According to a historical reconstruction, Bardylis founded a powerful Illyrian dynasty among the Dassaretii in the 5th century BC, and established a realm centered in their territory that comprised the area along Lychnidus and east to the Prespa Lakes, which was called "Dassaretis" later in Roman times. ; ; ; ; ; ; . A fragment of Callisthenes (c. 360 – 327 BC) which places Bardylis' realm between Molossis and Macedonia, well determines the position of that Illyrian kingdom in the area of Dassaretis. Bardylis' expansion in Upper Macedonia and Molossis, and his son Cleitus' revolt at Pelion in Dassaretis against Alexander the Great make this localization of the core of their realm even more plausible. There is also another historical reconstruction that considers Bardylis a Dardanian ruler, who during the expansion of his dominion included the region of Dassaretis in his realm, but this is considered an old fallacy because it is unsupported by any ancient source, while some facts and ancient geographical locations go squarely against it. The exact extension of the kingdom of Bardylis and Cleitus is not known, as it could have included other regions besides Dassaretis.

Bardylis, unlike previous Illyrian kings, developed both the military and economic aspects of his state. His subjects, the Damastini, began to issue fine silver coins from c. 395 BC in the Illyrian city of Damastion. These coins adopted a version of the standard coins and some emblems of the then powerful Chalcidian League. They also exported silver in ingot form. Other coins were issued from around 365 BC in Daparria, a mining city in what is now Kosovo which used the same standard and types as the coinage of the Damastini. The distribution of the coins suggest that Bardylis built up trade within the central Balkans and northwards to the Danube, which was far from the areas dominated by the Greek traders. Dionysius of Syracuse tried to take advantage of Bardylis's growing trade when he established colonies in the Adriatic. It is probable that Bardylis, unlike previous Illyrian kings, built a few fortified cities, for Lychnidus and Pelion in Lynkestis were walled sites probably before the accession of Philip.

Bardylis was an ambitious Illyrian ruler, who (according to Tertullian) after seeing a sign in a dream, embarked on a series of military victories which allowed him to extend Illyrian rule over the Molossians and other regional tribes, as far as the frontiers of Macedon.

Macedonian campaigns
It seems that Bardylis opposed the agreement between Amyntas III and Sirras and invaded Macedon in 393 BC. Bardylis used new warfare tactics never before used by any of the Illyrians. He won a decisive battle against Amyntas III, expelled him, and ruled Macedon through a puppet king. In 392 BC, Amyntas III allied himself with the Thessalians and was able to bring Macedon back under his rule from the Illyrians. However, the Illyrians were constantly raiding the northern frontiers of Macedon. After continuous invasions, Bardylis was able to force Macedon to pay him an annual tribute in 372 BC.

In 370 BC, the Macedonian king Amyntas III died, having restored the fortunes of his kingdom after the Illyrian disasters from earlier in his reign. His marriage to Eurydice, daughter of Lyncestian prince Sirras produced three sons and a daughter. His eldest son was Alexander II. In 369 BC, Bardylis prevented Alexander II from eliminating the Illyrians from Macedonia. After the battle, Bardylis was said to have briefly held Philip II, the youngest brother of Alexander II, as a hostage. In 368 BC, Alexander II was succeeded by his brother Perdiccas III.

The Paeonians began a series of raids against the Macedonians in support of a Illyrian invasion from the north. Perdiccas III, king of Macedonia, humiliated by the indignity of having to pay tribute to the Illyrians, marched north in the spring of 359 BC with the Macedonian army to resolve the issue by battle. This was not the first occasion in which he had fought against Bardylis, but the Macedonians lost the battle. The king himself was among the 4,000 Macedonian dead. The remainder, panic-stricken after having become exceedingly afraid of the Illyrian army, lost heart for continuing the war. This was the worst loss suffered by the Macedonians in their efforts to free themselves from the Illyrian invaders. The Illyrians followed up their victory by expanding their control southward to Lake Lychnitis (Lake Ohrid) and westward into Upper Macedonia. Through the actions of Bardylis, the Illyrians had brought Macedon close to collapse.

When Philip II, the youngest of the three brothers assumed the throne, he was determined to subdue the Illyrians under Bardylis once and for all, and thus destroy the Illyrian menace.

Raid on Epirus
In 385 BC, the Illyrians, who at that time were most likely under Bardylis' rule, formed an alliance with the powerful tyrant Dionysius of Syracuse. The purpose of the agreement was the restoration to the throne of the Molossian Alcetas who had become a refugee living at the court of Dionysius. Both sides were interested in the benefits of such an alliance as it would secure Illyrian power and weaken the influence of the Spartans and Macedonians in Epirus. This would also give Dionysius an opportunity to strengthen trade opportunities along the shores of the Adriatic and Ionian seas.

Dionysius sent 2,000 men and 5,000 weapons to the Illyrians who were prepared to go to war. With these new supplies, Bardylis and his army invaded Epirus and slaughtered 15,000 Molossians. However, this action came to naught after the Spartans under Agesilaus intervened and expelled the Illyrians from the region.

In 360 BC, another Illyrian attack forced the Molossian king Arymbas to evacuate his non-combatant population to Aetolia and he let the Illyrians loot the region again. The stratagem worked and the Molossians fell upon the Illyrians who were encumbered with booty and defeated them. In the same year Arymbas also defeated the Illyrians after they raided and looted Epirus.

Battle of Erigon Valley
In 359 BC, Macedon was able to return to the field of battle against the Illyrians, after it had overcome the internal state of political chaos and removed the risk of attack from other opponents. When Philip II assumed the Macedonian throne, substantial areas of upper Macedonia remained under the control of Bardylis. In order to concentrate on the internal struggle necessary to secure his crown, Philip reaffirmed the treaty the Illyrians had imposed on Macedonia by force of arms and sealed the alliance by his marriage to Audata, great-granddaughter of Bardylis. This action undoubtedly deterred a full-scale Illyrian invasion of Macedon at a time when the country was most vulnerable.

By the spring of 358 BC, Philip had at last secured his throne and was now able to address the occupation of north-west Macedon by Bardylis. When word of the mobilization of the Macedonian army came to Bardylis's attention, he proposed to Philip that they sign a treaty to maintain the status quo, provided that both parties maintain the cities that were already in their possession at the time. This was, of course, unacceptable to Philip because he was not prepared to accept any terms other than a full Illyrian withdrawal from north-west Macedonia. Bardylis, however, was not inclined to give up his gains without a fight. Philip mobilized every able-bodied soldier in Macedon for the battle. Bardylis, as before, was not likely to take any prisoners, so any Macedonian defeat would result in crippling casualties.

Although the two armies were almost equal in numbers – Bardylis's 500 cavalry and 10,000 infantry against Philip's force of 600 cavalry and 10,000 infantry – the Macedonians were far better trained and equipped. The armies met in battle on a plain in the Erigon Valley near Bitola, just south-east of Illyria. Bardylis initially deployed in a linear formation with his strongest troops in the centre, similar to the phalanx formation. Philip concentrated his best troops, the hypaspists, on his right flank. As Philip advanced to engage Bardylis, his cavalry turned one or two of Bardylis' flanks, forcing him to redeploy into a defensive square formation. The Illyrians for quite some time withstood the assaults of the enemy. At first neither party had control of the battlefield, and so the battle continued for a long time. Eventually Philip's hypaspists succeeded in penetrating the right corner of the Illyrian forces, which other Macedonian troops were able to widen. This threw Bardylis's entire formation into disorder, after which it was quickly broken by the phalanx and routed from the battlefield.

Thus Philip II of Macedon was able to defeat Bardylis. Diodorus Siculus (1st century BC) writes of the event:

''And at first for a long while the battle was evenly poised because of the exceeding gallantry displayed on both sides, and as many were slain and still more wounded, the fortune of battle vacillated first one way then the other, being constantly swayed by the valorous deeds of the combatants; but later as the horsemen pressed on from the flank and rear and Philip with the flower of his troops fought with true heroism, the mass of the Illyrians was compelled to take hastily to flight.When the pursuit had been kept up for a considerable distance and many had been slain in their flight, Philip recalled the Macedonians with the trumpet and erecting a trophy of victory buried his own dead, while the Illyrians, having sent ambassadors and withdrawn from all the Macedonian cities, obtained peace. But more than seven thousand Illyrians were slain in this battle.''

The battle had cost the Illyrian 7,000 casualties, almost three quarters of their initial army. Bardylis himself was probably killed in this battle as he rode on horseback at the advanced age of 90. Although the Macedonians finally won the battle, Philip II saw that he was not able to follow the enemy and chase them. The Illyrians later sent representatives and settled terms for peace, releasing all the cities they had conquered from Macedonia. In this battle, the troubling issue of Lynkestis was resolved, changing the situation in the western borders in favour of Macedonia. Philip secured Macedon's north-west frontier by annexing Illyrian territory as far as Lake Lynkcesta (Lake Ohrid). This would form a defensive buffer against any future Illyrian raids attempted through the Drilon Valley. The borders between the Illyrian and the Macedonians remained around Lake Ohrid for a long time.

Family
Bardylis had a son named Cleitus. Cleitus' son was presumably Bardylis II. The Illyrian princess Bircenna, who married the Molossian king Pyrrhus of Epirus (c. 290 BC), was the daughter of Bardylis II. Bardylis' daughter or granddaughter Audata married Philip II of Macedon.

title: Bardylis


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Question

A question is an utterance which serves as a request for information. Questions are sometimes distinguished from interrogatives, which are the grammatical forms typically used to express them. Rhetorical questions, for instance, are interrogative in form but may not be considered bona fide questions, as they are not expected to be answered.

Questions come in a number of varieties. Polar questions are those such as the English example "Is this a polar question?", which can be answered with "yes" or "no". Alternative questions such as "Is this a polar question, or an alternative question?" present a list of possibilities to choose from. Open questions such as "What kind of question is this?" allow many possible resolutions.

Questions are widely studied in linguistics and philosophy of language. In the subfield of pragmatics, questions are regarded as illocutionary acts which raise an issue to be resolved in discourse. In approaches to formal semantics such as alternative semantics or inquisitive semantics, questions are regarded as the denotations of interrogatives, and are typically identified as sets of the propositions which answer them.

Definitions
Linguistically, a question may be defined on three levels.

At the level of semantics, a question is defined by its ability to establish a set of logically possible answers.

At the level of pragmatics, a question is an illocutionary category of speech act which seeks to obtain information from the addressee.

At the level of syntax, the interrogative is a type of clause which is characteristically associated with questions, and defined by certain grammatical rules (such as subject–auxiliary inversion in English) which vary by language.

Some authors conflate these definitions. While prototypical questions (such as "What is your name?") will satisfy all three definitions, their overlap is not complete. For example "I would like to know your name." satisfies the pragmatic definition, but not the semantic or syntactic ones. Such mismatches of form and function are called indirect speech acts.

Uses
The principal use of questions is to elicit information from the person being addressed by indicating the information which the speaker (or writer) desires.

A slight variant is the display question, where the addressee is asked to produce information which is already known to the speaker. For example, a teacher or game show host might ask "What is the capital of Australia?" to test the knowledge of a student or contestant.

A direction question is one that seeks an instruction rather than factual information. It differs from a typical ("information") question in that the characteristic response is a directive rather than a declarative statement. For example:

 * A: When should I open your gift?
 * B: Open it now.

Questions may also be used as the basis for a number of indirect speech acts. For example, the imperative sentence "Pass the salt." can be reformulated (somewhat more politely) as:

 * Would you pass the salt?

Which has the form of an interrogative, but the illocutionary force of a directive.

The term rhetorical question may be colloquially applied to a number of uses of questions where the speaker does not seek or expect an answer (perhaps because the answer is implied or obvious), such as:

 * Has he lost his mind?
 * Why have I brought you all here? Let me explain...
 * They're closed? But the website said it was open until 10 o'clock.

Loaded questions (a special case of complex questions), such as "Have you stopped beating your wife?" may be used as a joke or to embarrass an audience, because any answer a person could give would imply more information than he was willing to affirm.

Semantic classification
The main semantic classification of questions is according to the set of logically possible answers that they admit. An open question, such as "What is your name?", allows indefinitely many possible answers. A closed question admits a finite number of possible answers. Closed questions may be further subdivided into yes–no questions (such as "Are you hungry?") and alternative questions (such as "Do you want jam or marmalade?").

The distinction between these classes tends to be grammaticalized. In English, open and closed interrogatives are distinct clause types characteristically associated with open and closed questions, respectively.

Yes–no questions
A yes–no question (also called a polar question, or general question ) asks whether some statement is true. They can in principle be answered by a "yes" or "no" (or similar words or expressions in other languages). Examples include "Do you take sugar?", "Should they be believed?" and "Am I the loneliest person in the world?"

Alternative questions
An alternative question presents two or more discrete choices as possible answers in an assumption that only one of them is true. For example:

 * Are you supporting England, Ireland or Wales?

The canonical expected answer to such a question would be either "England", "Ireland", or "Wales". Such an alternative question presupposes that the addressee supports one of these three teams. The addressee may cancel this presupposition with an answer like "None of them".

In English, alternative questions are not syntactically distinguished from yes–no questions. Depending on context, the same question may have either interpretation: In speech, these are distinguishable by intonation.
 * Do these muffins have butter or margarine? [I'm on a low fat diet.]
 * Do these muffins have butter or margarine? [I saw that the recipe said you could use either.]

Open questions
An open question (also called a variable question, non-polar question, or special question ) admits indefinitely many possible answers. For example:

 * Where should we go for lunch?

In English, these are typically embodied in a closed interrogative clause, which uses an interrogative word such as when, who, or what. These are also called wh-words, and for this reason open questions may also be called wh-questions.

Question formation
Questions may be marked by some combination of word order, morphology, interrogative words, and intonation. Where languages have one or more clause type characteristically used to form questions, they are called interrogative clauses. Open and closed questions are generally distinguished grammatically, with the former identified by the use of interrogative words.

In English, German, French and various other (mostly European) languages, both forms of interrogative are subject to an inversion of word order between verb and subject. In English, the inversion is limited to auxiliary verbs, which sometimes necessitates the addition of the auxiliary do, as in:

 * a. Sam reads the newspaper. - Statement
 * b. Does Sam read the newspaper? - Yes–no question formed using inversion and do-support

Open questions
Open questions are formed by the use of interrogative words such as, in English, when, what, or which. These stand in as variables representing the unknown information being sought. They may also combine with other words to form interrogative phrases, such as which shoes in:

 * Which shoes should I wear to the party?

In many languages, including English and most other European languages, the interrogative phrase must (with certain exceptions such as echo questions) appear at the beginning of the sentence, a phenomenon known as wh-fronting. In other languages, the interrogative appears in the same position as it would in a corresponding declarative sentence (in situ).

A question may include multiple variables as in:

 * Whose gifts are in which boxes?

Polar questions
Different languages may use different mechanisms to distinguish polar ("yes-no") questions from declarative statements (in addition to the question mark). English is one of a small number of languages which use word order. Another example is French:

Cross-linguistically, the most common method of marking a polar question is with an interrogative particle, such as the Japanese か ka, Mandarin 吗 ma and Polish czy.

Other languages use verbal morphology, such as the -n verbal postfix in the Tunica language.

Of the languages examined in the World Atlas of Language Structures, only one, Atatláhuca–San Miguel Mixtec, was found to have no distinction between declaratives and polar questions.

Intonation
Most languages have an intonational pattern which is characteristic of questions (often involving a raised pitch at the end, as in English).

In some languages, such as Italian, intonation is the sole distinction.

In some languages, such as English, or Russian, a rising declarative is a sentence which is syntactically declarative but is understood as a question by the use of a rising intonation. For example, "You're not using this?"

On the other hand, there are English dialects (Southern Californian English, New Zealand English) in which rising declaratives (the "uptalk") do not constitute questions. However it is established that in English there is a distinction between assertive rising declaratives and inquisitive rising declaratives, distinguished by their prosody.

Request for confirmation and speaker presupposition
Questions may be phrased as a request for confirmation for a statement the interrogator already believes to be true.

A tag question is a polar question formed by the addition of an interrogative fragment (the "tag") to a (typically declarative) clause. For example:

 * You're John, aren't you?
 * Let's have a drink, shall we?
 * You remembered the eggs, right?

This form may incorporate speaker's presupposition when it constitutes a complex question. Consider a statement
 * (A) Somebody killed the cat

and several questions related to it.
 * (B) John killed the cat, did he? (tag question)
 * (C) Was it John who killed the cat?

As compared with:
 * (D) Who killed the cat?

Unlike (B), questions (C) and (D) incorporate a presupposition that somebody killed the cat.

Question (C) indicates speaker's commitment to the truth of the statement that somebody killed the cat, but no commitment as to whether John did it or did not.

Punctuation
In languages written in Latin, Cyrillic or certain other scripts, a question mark at the end of a sentence identifies questions in writing. As with intonation, this feature is not restricted to sentences having the grammatical form of questions – it may also indicate a sentence's pragmatic function.

In Spanish an additional ¿|inverted mark is placed at the beginning: ¿Cómo está usted? "How are you?". An uncommon variant of the question mark is the interrobang (‽), which combines the function of the question mark and the exclamation mark.

Responses and answers
The Cambridge Grammar of the English Language distinguishes between an answer (being a member of the set of logically possible answers, as delineated in ) and a response (any statement made by the addressee in reply to the question). For example, the following are all possible responses to the question "Is Alice ready to leave?"

i. (a) Yes. (b) She's ready. (c) No, she's not. ii. (a) I don't know. (b) Why do you ask? (c) She might be. iii.(a) She's still looking for her wallet. (b) She wasn't expecting you before 5 o'clock. (c) I'll let you know when she's ready.

Only the [i] responses are answers in the Cambridge sense. The responses in [ii] avoid committing to a yes or no answer. The responses in [iii] all implicate an answer of no, but are not logically equivalent to no. (For example, in [iiib], the respondent can cancel the implicature by adding a statement like: "Fortunately, she packed everything up early.")

Along similar lines, Belnap and Steel (1976) define the concept of a direct answer:

A direct answer to a given question is a piece of language that completely, but just completely, answers the question...What is crucial is that it be effectively decidable whether a piece of language is a direct answer to a specific question... To each clear question there corresponds a set of statements which are directly responsive. ... A direct answer must provide an unarguably final resolution of the question.

Answering negative questions
"Negative questions" are interrogative sentences which contain negation in their phrasing, such as "Shouldn't you be working?" These can have different ways of expressing affirmation and denial from the standard form of question, and they can be confusing, since it is sometimes unclear whether the answer should be the opposite of the answer to the non-negated question. For example, if one does not have a passport, both "Do you have a passport?" and "Don't you have a passport?" are properly answered with "No", despite apparently asking opposite questions. The Japanese and Korean languages avoid this ambiguity. Answering "No" to the second of these in Japanese or Korean would mean, "I do have a passport".

A similar ambiguous question in English is "Do you mind if...?" The responder may reply unambiguously "Yes, I do mind," if they do mind, or "No, I don't mind," if they don't, but a simple "No" or "Yes" answer can lead to confusion, as a single "No" can seem like a "Yes, I do mind" (as in "No, please don't do that"), and a "Yes" can seem like a "No, I don't mind" (as in "Yes, go ahead"). An easy way to bypass this confusion would be to ask a non-negative question, such as "Is it all right with you if...?"

Some languages have different particles (for example the French "si", the German "doch" or the Swedish, Danish, and Norwegian "jo") to answer negative questions (or negative statements) in an affirmative way; they provide a means to express contradiction.

Indirect questions
As well as direct questions (such as Where are my keys?), there also exist indirect questions (also called interrogative content clauses), such as where my keys are. These are used as subordinate clauses in sentences such as "I wonder where my keys are" and "Ask him where my keys are." Indirect questions do not necessarily follow the same rules of grammar as direct questions. For example, in English and some other languages, indirect questions are formed without inversion of subject and verb (compare the word order in "where are they?" and "(I wonder) where they are"). Indirect questions may also be subject to the changes of tense and other changes that apply generally to indirect speech.

Learning
Questions are used from the most elementary stage of learning to original research. In the scientific method, a question often forms the basis of the investigation and can be considered a transition between the observation and hypothesis stages. Students of all ages use questions in their learning of topics, and the skill of having learners creating "investigatable" questions is a central part of inquiry education. The Socratic method of questioning student responses may be used by a teacher to lead the student towards the truth without direct instruction, and also helps students to form logical conclusions.

A widespread and accepted use of questions in an educational context is the assessment of students' knowledge through exams.

Origins
Enculturated apes Kanzi, Washoe, Sarah and a few others who underwent extensive language training programs (with the use of gestures and other visual forms of communications) successfully learned to answer quite complex questions and requests (including question words "who", "what", "where"), although so far they have failed to learn how to ask questions themselves. For example, David and Anne Premack wrote: "Though she [Sarah] understood the question, she did not herself ask any questions — unlike the child who asks interminable questions, such as What that? Who making noise? When Daddy come home? Me go Granny's house? Where puppy? Sarah never delayed the departure of her trainer after her lessons by asking where the trainer was going, when she was returning, or anything else". The ability to ask questions is often assessed in relation to comprehension of syntactic structures. It is widely accepted that the first questions are asked by humans during their early infancy, at the pre-syntactic, one word stage of language development, with the use of question intonation.

title: Question


text:
John Penn

John Penn may refer to:

 * Descendants of William Penn, 1st proprietor of Pennsylvania:
 * John Penn ("the American") (1700–1746), son of William Penn, 2nd proprietor of Pennsylvania, inherited 50% interest in Colony, died childless
 * John Penn (governor) (1729–1795), son of Richard Penn, Sr. and grandson of William Penn, colonial governor of Pennsylvania, his 25% interest in the Colony was lost in the American Revolution
 * John Penn (writer) (1760–1834), also called John Penn of Stoke, son of Thomas Penn and grandson of William Penn, 5th proprietor of Pennsylvania, his 75% interest in the Colony was lost in the American Revolution
 * John Penn (North Carolina politician) (1741–1788), Continental Congressman from North Carolina, signer of the Declaration of Independence
 * John Penn (engineer) (1805–1878), British marine engine engineer
 * John Penn (Conservative politician) (1848–1903), MP for Lewisham 1891–1903
 * John Penn (architect) (1921–2007), British architect
 * Jack Penn (1909–1996), surgeon
 * John Garrett Penn (1932–2007), United States federal judge
 * John S. Penn (born 1926), American politician the New Jersey General Assembly
 * John Penne, MP for Melcombe Regis

title: John Penn