title: Viktor Korchnoi

text: Korchnoi won at Havana 1963 with 16½/21, but fared less well in the next Soviet Championship, URS-ch31 at Leningrad, with just 10/19 for 10th place. He missed qualifying for the next world championship cycle, 1964–66, because of a poor showing at the 1964 Zonal tournament in Moscow, where he made 5½/12 for a shared 5th–6th place, so did not advance to the Interzonal. Korchnoi regained his form with an overwhelming triumph at Gyula, Hungary, in 1965 with 14½/15. He won at Bucharest 1966 with 12½/14, and at the Chigorin Memorial in Sochi 1966 with 11½/15.


title: Viktor Korchnoi

text: In the 1969 World Championship cycle, he tied for 3rd–5th places at the URS-ch34, held at Tbilisi 1966–67, with 12/20, and emerged from a three-way playoff, along with GM Aivars Gipslis, at Tallinn 1967, to the Interzonal, staged at Sousse, Tunisia, later that year. A strong performance at the Interzonal, with 14/22, for a shared 2nd–4th place, took him through to the Candidates' matches. In his first match, he defeated American GM Samuel Reshevsky at Amsterdam in 1968 by (+3=5). His next opponent was GM Mikhail Tal, against whom Korchnoi had a large plus score in previous meetings. The match, held in Moscow 1968, was close, but Korchnoi won by (+2−1=7), and moved on to face GM Boris Spassky in the Candidates' final. Spassky prevailed at Kiev 1968, winning (+4−1=5).


title: Viktor Korchnoi

text: Korchnoi represented the USSR on board three in the first Russia (USSR) vs. Rest of the World team match, Belgrade 1970, which took place across ten boards. He played four games with Hungarian GM Lajos Portisch, drawing three and losing one. In 1984, eight years after his defection, Korchnoi played board three in the second Rest of the World vs USSR match in London, with the match again held across ten boards. He faced Soviet GM Lev Polugaevsky, his former teammate, in three games, winning one and drawing two; he then faced GM Vladimir Tukmakov in one game, drawing. Korchnoi was the only player to play for each side in the series of two team matches.


title: Viktor Korchnoi

text: Korchnoi, as the losing finalist, was exempt from qualifying for the 1972 World Chess Championship, and was seeded directly to the following Candidates' event. To prepare, he first played a secret training match with his good friend GM David Bronstein, who drew the 1951 World Championship match, in Leningrad 1970, losing 3½–2½. This result was kept secret until 1995; the games from this match were kept secret until 2007, when they were eventually published in Bronstein's last book, "Secret Notes". Then, he played a secret training match against GM Anatoly Karpov, with whom he was close friends at the time, at Leningrad 1971; this wound up drawn in six games (+2−2=2); Korchnoi took the Black pieces in five of them, for training purposes. These games were eventually published in 1976.


title: Viktor Korchnoi

text: Korchnoi won his first round 1971 match against GM Efim Geller at Moscow by (+4−1=3), after which he went down to defeat in the semifinal versus GM Tigran Petrosian by (−1=9), also at Moscow, with the ninth game the only decisive result.


title: Viktor Korchnoi

text: In 1972, Korchnoi appeared in the chess-themed Soviet film "Grossmeister" along with several other grandmasters; he played the role of the lead actor's trainer.


title: Viktor Korchnoi

text: In the 1975 World Championship cycle Korchnoi and Karpov, the newest star of Soviet chess, tied for first in the 1973 Leningrad Interzonal. In the 1974 Candidates' matches, Korchnoi first defeated the young Brazilian star GM Henrique Costa Mecking (who had won the other Interzonal in Petrópolis), by (+3−1=9) at Augusta, Georgia, in what he later described in his autobiography as a tough match. Korchnoi next played Petrosian again, at Odessa. The two were not on friendly terms, and it was even rumored that the two resorted to kicking each other under the table during this match; however, Korchnoi denies this. According to him, Petrosian just kicked his legs nervously and shook the table. Although the match was supposed to go to the first player to win four games, Petrosian resigned the match after just five games, with Korchnoi enjoying a lead of 3–1, with one draw.


title: Viktor Korchnoi

text: With his victory over Petrosian, Korchnoi advanced to face Karpov in the Candidates' Final, the match to determine who would challenge reigning world champion Bobby Fischer in 1975. In the run-up to the match, Korchnoi was constantly subjected to threats and harassment, and was virtually unable to find any Grandmasters to assist him. Bronstein apparently assisted Korchnoi, for which he was punished. Bronstein, in his last book, "Secret Notes", published in 2007, wrote that he advised Korchnoi before the match began, but then had to leave to play an event himself; when he returned, Korchnoi was down by three games. Bronstein then assisted Korchnoi for the final stages. Korchnoi also received some assistance later in the match from two British masters, IM (later GM) Raymond Keene and IM William Hartston. Korchnoi trailed 3–0 late in the match, but won games 19 and 21 to make it very close right to the end. Karpov eventually won this battle, played in late 1974 in Moscow, by a 12½–11½ score. By default, Karpov became the twelfth world champion in April 1975, when Fischer refused to defend his title because of disputed match conditions.


title: Viktor Korchnoi

text: In the lead-up to the Candidates' Final in 1974, as part of a campaign to promote Karpov over Korchnoi, Tigran Petrosian made a public statement in the press against Korchnoi, with the Soviet federation, wishing to develop younger players, taking the stance that the generation (including Korchnoi) which had been defeated by Bobby Fischer could no longer hope to compete successfully against him. At the closing ceremony of the Candidates' Final, Korchnoi had made his mind up that he had to leave the Soviet Union. The central authorities prevented Korchnoi from playing any international tournaments outside the USSR. Even when Korchnoi was invited by GM Paul Keres and IM Iivo Nei to participate in a 1975 International Tournament in the Estonian SSR, Korchnoi was not allowed to play, and both Keres and Nei were reprimanded.


title: Viktor Korchnoi

text: Keres did play a short, apparently secret, training match at Tallinn 1975 with Korchnoi, who won (+1=1). Korchnoi was then allowed to play the Soviet Team Championship and an international tournament in Moscow later in 1975. The ban against Korchnoi competing outside the USSR was lifted when he accompanied fellow veteran GMs Mark Taimanov and Bronstein to London to play a Scheveningen-style event (where each team member competes against only the other team's players) against three young British masters: Jonathan Mestel, Michael Stean and David S. Goodman. Korchnoi then played the international tournament at Hastings, 1975–76.


title: Viktor Korchnoi

text: Korchnoi, in a 2006 lecture in London, mentioned that the breakthrough that allowed him to resume international appearances came when Anatoly Karpov inherited the World Championship title forfeited by Fischer. Questions arose about how Karpov had qualified to be a World Champion, when he had never played Fischer. Since Korchnoi was not publicly visible, it was largely believed that he (and Karpov) could not be very strong. Korchnoi was then allowed to play the 1976 Amsterdam tournament, as a means to prove Karpov was a worthy World Champion.


title: Viktor Korchnoi

text: Korchnoi was joint winner of the tournament, along with GM Tony Miles. At the end of the tournament, Korchnoi asked Miles to spell "political asylum" for him, whereupon Korchnoi entered the police station to defect, having smuggled his chess library out of the USSR in two stages, on this trip as well as the previous year's trip to England. As a result, after the chess tournament in Amsterdam, Korchnoi became the first strong Soviet grandmaster to defect from the Soviet Union. Korchnoi left his wife and son behind. The defection resulted in a turbulent period of excellent tournament results, losses in the two matches for the World Title, all overshadowed by the oppressive political climate of the Cold War.


title: Viktor Korchnoi

text: Korchnoi resided in the Netherlands for some time, giving simultaneous exhibitions. He played a short match against GM Jan Timman – the strongest active non-Soviet player at that time – and comprehensively defeated him. He moved to West Germany for a short period, and then eventually settled in Switzerland by 1978, becoming a Swiss citizen.


title: Viktor Korchnoi

text: In the next world championship cycle (1976–78), for which he qualified as the losing finalist, Korchnoi first had to overcome Soviet demands that he be forfeited due to his defection; FIDE President GM Max Euwe defended Korchnoi's right to participate. Korchnoi began actual play by again vanquishing Petrosian, by (+2−1=9) in the quarter-final round at Il Ciocco, Italy, taking a clinching draw in a clearly favourable position in the final game. In the semifinal, held at Evian, France, Korchnoi won against GM Lev Polugaevsky, with a score of (+5−1=7). The final, in which he faced Spassky at Belgrade, began with five wins and five draws for Korchnoi, after which he lost four consecutive games. The match was noteworthy for Spassky's scandalous psychological behavior after game 10 where Spassky refused to play at the game board, instead analyzing the game from a demonstration board while seated in a box located behind Korchnoi. Ultimately, Korchnoi steeled himself and finally secured victory in the match by (+7−4=7) to emerge as the challenger to Karpov, having defeated three world-class Soviet contenders.


title: Viktor Korchnoi

text: The World Championship match of 1978 was held in Baguio, Philippines. There was enormous controversy off the board, ranging from X-raying of chairs, protests about the flags used on the board, hypnotism complaints and the mirror glasses used by Korchnoi. When Karpov's team sent him a bilberry yogurt during a game without any request for one by Karpov, the Korchnoi team protested, claiming it could be some kind of code (such as whether Korchnoi was dead equal or slightly advantageous). They later said this was intended as a parody of earlier protests, but it was taken seriously at the time.


title: Viktor Korchnoi

text: In quality of play, the match itself never measured up to the press headlines that it generated, although as a sporting contest it had its share of excitement. The match would go to the first player to win six games, draws not counting. After 17 games, Karpov had an imposing 4–1 lead. Korchnoi won game 21, but Karpov won game 27, putting him on the brink of victory with a 5–2 lead. Korchnoi bravely fought back, scoring three wins and one draw in the next four games, to equalise the match at 5–5 after 31 games. However, Karpov won the very next game, and the match, by 6–5 with 21 draws.


title: Viktor Korchnoi

text: Korchnoi alleged that when acting as his second in this match, Raymond Keene broke his contract by writing a book about the match (which appeared three days after the match finished) having specifically signed an agreement "not to write, compile or help to write or compile any book during the course of the match". Korchnoi commented: "Mr Keene betrayed me. He violated the contract. It was clear that while Mr Keene was writing one book and then another, Mr Stean was doing his work for him."


title: Viktor Korchnoi

text: Attempts to defend Keene were rebutted by Michael Stean's mother, who stated that she was in a position to know what was in Keene's contract since she herself had typed it. Keene, she claimed, had signed this despite having already negotiated a contract with Batsford to write a book about the match. She described "a premeditated and deliberate plan to deceive" and noted that Keene's conduct had come under suspicion during the match.


title: Viktor Korchnoi

text: As the losing world title match finalist, Korchnoi was seeded into the next cycle's final eight players. In his first match, Korchnoi once more defeated Petrosian in March 1980 at Velden am Wörthersee, Austria, by 5½ to 3½. This victory earned him a rematch with Polugaevsky, whom he had defeated in the previous cycle. At Buenos Aires during July and August 1980, Korchnoi again triumphed by 7½ to 6½; the match was tied following the regulation ten games. In the final match, at Meran, Italy, from December 1980 to January 1981, Korchnoi was leading West German GM Robert Hübner by 4½ to 3½, with two more possible regulation games to come, when Hübner withdrew from the match. This forfeit advanced Korchnoi to a rematch for the title against Karpov.


title: Viktor Korchnoi

text: This final match was also held in Meran, Italy. In what was dubbed the "Massacre in Meran", Karpov defeated Korchnoi by six wins to two, with ten draws.


title: Viktor Korchnoi

text: The headlines of the tournament again largely centered on the political issues. Korchnoi's wife and son had been denied emigration and were still in the Soviet Union. In 1980, his son had been promised release to join his father in exile if he gave up his passport. When he did so, he was promptly drafted into the Soviet army. In spite of protests, he was arrested for evading army service, sentenced to two and a half years in a labour camp, and served the full sentence. After his release in 1982, Korchnoi's wife and son were allowed to leave the USSR. Korchnoi divorced his wife soon after. At the time he was living with a Soviet émigrée whom he would later marry.


title: Viktor Korchnoi

text: Korchnoi still had a vital part to play in the next (1984) Candidates' cycle, although he never reached the highest pinnacle again. In the first match, he defeated GM Lajos Portisch by 6–3 at Bad Kissingen 1983. In the second round, he was to play the young Soviet GM Garry Kasparov, who at the time was battling against a Soviet Chess Federation that was clearly in favour of Anatoly Karpov. The match was to be held in Pasadena, California, but the Soviet Chess Federation protested (possibly because Korchnoi was a defector and the match was in the cold-war enemy's back yard, in a place closed to Soviet diplomats at the time, and because of the soon-to-be-announced Soviet decision to boycott the 1984 Olympic Games in Los Angeles). Kasparov was not allowed to fly there to play the match. This defaulted the match to Korchnoi. Another Soviet contender, former Champion GM Vassily Smyslov, was also at first forfeited to Hungarian GM Zoltan Ribli, with their match set to take place in the United Arab Emirates.


title: Viktor Korchnoi

text: However, upon intervention by prominent British chess organizer GM Raymond Keene, who quickly stepped up to raise a large amount of sponsorship money to save the troubled matches, Korchnoi agreed to play Kasparov in London, which at the same time also hosted the Smyslov vs. Ribli match. This was a gracious gesture by Korchnoi, since technically he had already won by default. After a good start, winning the first game, Korchnoi was beaten by a score of 7–4, with Kasparov, who was 32 years younger, proving that his all-round game and youthful stamina were too strong.


title: Viktor Korchnoi

text: After the 1983 Kasparov match, Korchnoi continued playing at a top level but without seriously threatening the world championship again. In the 1985–87 World Championship cycle he finished equal thirteenth out of 16 in the Candidates' Tournament at Montpellier.


title: Viktor Korchnoi

text: In the 1988–90 cycle, he made the final 16 again, but was eliminated in the first round of Candidates' matches, held at Saint John, New Brunswick, Canada, losing to Iceland's GM Johann Hjartarson in extra games, by 4½ to 3½.


title: Viktor Korchnoi

text: Finally, in the 1991–93 cycle, he defeated GM Gyula Sax of Hungary in the first round, by 5½ to 4½ at Wijk aan Zee. Then, at Brussels, he lost to GM Jan Timman of the Netherlands by 4½ to 2½, ending his run at the world championship, which stretched from 1962 to 1991.


title: Viktor Korchnoi

text: He continued to play in Europe and around the world to an advanced age, living in his adopted country of Switzerland. He frequently represented their Olympiad team on top board, beginning in 1978, even though his Elo rating was sometimes considerably below that of compatriot Vadim Milov, who appeared not to make himself available for selection.


title: Viktor Korchnoi

text: From 2001 onwards, Korchnoi became a prolific author of books on his career, publishing five new volumes, including two books of annotated games, an updated autobiography, and an overview (along with several other authors) of Soviet politics applying to chess; he also wrote a book on rook endings.


title: Viktor Korchnoi

text: In 2001, Korchnoi won the Biel Chess Festival for the second time in the grandmasters division, having also won in 1979. This 22-year gap still stands as the longest time period between being champion at Biel tournament, or quite possibly any international chess tournament.


title: Viktor Korchnoi

text: In September 2006, Korchnoi won the 16th World Senior Chess Championship, held in Arvier (Valle d'Aosta, Italy), at age 75, with a 9–2 score. Korchnoi scored 7½–½ in his first eight games, then drew his last three games.


title: Viktor Korchnoi

text: On the January 2007 FIDE rating list Korchnoi was ranked number 85 in the world at age 75, the oldest player ever to be ranked in the FIDE top 100. The second-oldest player on the January 2007 list was Alexander Beliavsky, age 53, who was 22 years younger than Korchnoi. As of 2011, Korchnoi was still active in the chess world with a notable win (in Gibraltar) with black against the 18-year-old Fabiano Caruana, who was rated above 2700 and 61 years Korchnoi's junior.


title: Viktor Korchnoi

text: Korchnoi became the oldest player ever to win a national championship, when he won the 2009 Swiss championship at age 78. He won the national title again a few months after his 80th birthday in July 2011 after a playoff game with Joseph Gallagher.


title: Viktor Korchnoi

text: In late December 2012, it was reported that Korchnoi was recovering from a stroke and was unlikely to play competitive chess again. He was scheduled to play in the 37th Zurich Christmas Open tournament in December 2013, but withdrew due to health reasons.


title: Viktor Korchnoi

text: However, in 2014, he returned to the board to play a two-game match against GM Wolfgang Uhlmann (1935–2020), winning both games; the combined age of the two players was 162 years, which is almost certainly a record for a standard play match between Grandmasters. In 2015 the two played a four-game rapid play match (25 minutes per player for all moves, plus 30 seconds extra per move), which was drawn 2–2. Korchnoi's final match against another Grandmaster was a similar four-game rapid play match in November 2015, against GM Mark Taimanov (1926–2016) – the first time since 1980 that Korchnoi had played in an official or friendly match against an opponent older than himself. The combined ages of the players was 174. Korchnoi won the match 2–1 with one draw.


title: Viktor Korchnoi

text: FIDE President Kirsan Ilyumzhinov said that Korchnoi "has contributed substantially to the popularisation of our sport and is considered rightly as one of the strongest and charismatic players in the entire history of world chess". One obituary, written by Leonard Barden, called him "the greatest player never to have been world champion".


title: Viktor Korchnoi

text: Korchnoi was comfortable playing with or without the initiative. He could attack, counterattack, play positionally, and was a master of the endgame. He became known as the master of counterattack, and he was the most difficult opponent of Mikhail Tal, an out-and-out attacker. He had a large lifetime plus score against Tal (+13−4=17), and also had plus scores against world champions Petrosian and Spassky. He had equal records against Botvinnik (+1−1=2) and Fischer (+2−2=4). He defeated nine undisputed world champions from Botvinnik through to Garry Kasparov, and Magnus Carlsen.


title: Viktor Korchnoi

text: At times Korchnoi displayed his temper after losing games by sweeping all the pieces off the board. Among his colleagues, he had a reputation of being short tempered. At times, however, he displayed genial manners. In the 1983 U.S. Open Chess Championship in Pasadena, California, Korchnoi was paired against GM Larry Christiansen who was late showing up to the game when his "old jalopy" car ran out of gas on the way to the event. Rather than starting Christiansen's clock, Korchnoi waited until Christiansen arrived—a very kind gesture indeed.


title: Viktor Korchnoi

text: Korchnoi never succeeded in becoming world chess champion, but many people consider him the strongest player never to have done so, a distinction also often attributed to Akiba Rubinstein and Paul Keres. On the other hand, the 10th world champion Boris Spassky argued that Korchnoi did not deserve to be champion, both because he did not play the best moves (sometimes taking 140 moves to win a game that could have been won in 40), and because he did not have any individuality.


title: Viktor Korchnoi

text: One of the variations of the English Opening is called the Korchnoi Variation, a variation for White against the French Defense is called the Korchnoi Gambit and a closed variation of the Sicilian Defense is called the Korchnoi Defense.


title: Viktor Korchnoi

text: Korchnoi defeated nine undisputed world champions (Botvinnik, Smyslov, Tal, Petrosian, Spassky, Fischer, Karpov, Kasparov and Carlsen), a record he shares with Paul Keres and Alexander Beliavsky.


title: Viktor Korchnoi

text: He is the only player to have won or drawn—in individual game(s)—against every World Chess Champion, disputed or undisputed, since the world chess championship interregnum of World War II.


title: Vanderbilt Commodores football

text: The Vanderbilt Commodores football program represents Vanderbilt University in the sport of American football. The Commodores compete in the Football Bowl Subdivision (FBS) of the National Collegiate Athletic Association (NCAA) and the East Division of the Southeastern Conference (SEC). They are led by head coach Clark Lea. Vanderbilt plays their home games at FirstBank Stadium, located on the university's Nashville, Tennessee campus.


title: Vanderbilt Commodores football

text: Vanderbilt has a winning percentage of .492, the second lowest among all members in the Southeastern Conference.


title: Vanderbilt Commodores football

text: Adopting the nickname the "Commodores" after the 1897 season, the team has played in 1,250 games over 126 seasons. In that time, six coaches have led the Commodores to a postseason bowl appearance: Art Guepe, Steve Sloan, George MacIntyre, Bobby Johnson, James Franklin and Derek Mason. Four have led them to a conference championship: R. G. Acton, W. H. Watkins, James R. Henry, and Dan McGugin. McGugin is the leader in seasons coached and games won, with 198 victories during his 30 years at Vanderbilt. He was awarded two National Championships retroactively by Clyde Berryman.


title: Vanderbilt Commodores football

text: Of the 29 different head coaches who have led the Commodores, McGugin, Ray Morrison, Red Sanders, and Bill Edwards have been inducted into the College Football Hall of Fame.


title: Vanderbilt Commodores football

text: Vanderbilt has been invited to nine bowl games, with the Commodores garnering a record of 4–4–1 in bowl games.


title: Vanderbilt Commodores football

text: , Tennessee leads the series 75–32-5. When the rivalry first started Vanderbilt dominated by taking 19 of the first 24 with 3 ties. From 1928 to 2011, Tennessee went 71–9–2 against Vanderbilt. But since 2012, Vanderbilt has won five of the last nine. The largest margin of victory for Vanderbilt was by 76 points in 1918 at Old Dudley Field in Nashville. (Vanderbilt 76, Tennessee 0) The largest defeat was 65 points in 1994 at Vanderbilt Stadium (Tennessee 65, Vanderbilt 0). The longest win streaks for Vanderbilt is (9) from 1901 to 1913. The longest win streak for Tennessee is 22, from 1983 to 2004.


title: Vanderbilt Commodores football

text: Having started in 1893, the Georgia-Vanderbilt football series has been played annually since 1968. The two are divisional opponents in the SEC East. The series, which rotates between Nashville, Tennessee, and Athens, Georgia, stands with Georgia leading 60-20–2 through the 2022 season.


title: Vanderbilt Commodores football

text: Vanderbilt and Ole Miss have played 92 times since 1894. Ole Miss leads the series 51-39-2. The largest margin of victory was by 91 points won by Vanderbilt in 1915. Vanderbilt also holds the longest win streaks in the series (18) from 1894 to 1938.


title: Vanderbilt Commodores football

text: Having started in 1896, the Kentucky-Vanderbilt football series has been played annually since 1953. The two are divisional opponents in the SEC East. The series, which rotates between Nashville, Tennessee and Lexington, Kentucky, is led by Kentucky at 48-42–4 with the average score being Vanderbilt 16.9-Kentucky 15.6.


title: Vanderbilt Commodores football

text: The Commodores first met the Georgia Tech Yellow Jackets in 1892 in Atlanta, Georgia with Vanderbilt winning 20–10. Since 1924, the winning team in the series has received a silver-plated cowbell with the year and final score of each game engraved on it. The trophy was created by Ed F. Cavaleri, who was described by the Atlanta Constitution as “a faithful Georgia Tech supporter though he did not attend the Jacket institution,” according to Georgia Tech's website. Cavaleri purchased a cowbell at an Atlanta hardware store to use as a noise-maker while on his way to a game in 1924. The Commodores defeated Georgia Tech 3–0, however another fan in attendance suggested that Cavaleri award the bell to the winning team. The tradition was born and Cavaleri attended every game between the two teams from 1924 to 1967. The cowbell has a gold plate screwed into each side, with “GEORGIA TECH-VANDERBILT FOOTBALL TROPHY” inscribed at the top. Three columns list the year of each game, Vanderbilt’s points scored and Georgia Tech’s points scored. The results of the games from 1924 to 1967 are engraved on one side; the results from 2002, 2003, 2009 and 2016 are on the other. Vanderbilt is 15–20-3 against Georgia Tech in 38 games. The Commodores lost the last matchup 38–7 in 2016.


title: Vanderbilt Commodores football

text: Vanderbilt and the Sewanee Tigers were both founding members of the Southern Intercollegiate Athletic Association (SIAA), the Southern Conference, and the Southeastern Conference (SEC). It is the oldest of Vanderbilt's rivalries; dating back to 1891 when Vanderbilt played its second football game. Vanderbilt leads the series 40–8–4. The largest margin of victory was in 1905 when Vanderbilt won 68–4. Usually played towards the end of the season on Thanksgiving Day, the two teams have not met again since 1944 and are unlikely to anytime soon as Sewanee plays in NCAA Division III.


title: Vanderbilt Commodores football

text: Traditionally, Vanderbilt has featured differing designs of gold helmets, black jerseys, and gold or black pants at home, and gold helmets, white jerseys and gold, or white pants on the road. Meanwhile, the traditional alternate uniform saw gold helmets and jerseys matched with white pants.


title: Vanderbilt Commodores football

text: The James Franklin (2011–2013), and Derek Mason (2014-2020) eras saw the introduction of several new combinations- including "blackout” (i.e. all black), and "whiteout" (i.e. all white) uniforms. The team's gold alternate jerseys were also re-designed with the addition of black shoulders and a more muted gold. Eventually, battleship gray was incorporated as well.


title: Vanderbilt Commodores football

text: The Clark Lea (2021–Present) era has seen a return to traditional gold helmets (note: featuring a modernized “V” logo), black jerseys, and gold pants at home, and gold helmets, white jerseys, and gold (or white) pants on the road. Vanderbilt introduced a new all white ("whiteout") uniform at the start of the 2022 season.


title: Vanderbilt Commodores football

text: Vanderbilt Commodore football personnel have been inducted into the National Football Foundation's National College Football Hall of Fame.


title: Vanderbilt Commodores football

text: Vanderilt has had seven consensus All-Americans in their history. In 2016, Zach Cunningham became the first unanimous All-American in Vanderbilt's history.


title: Vanderbilt Commodores football

text: Vanderbilt Commodores personnel, including coaches and players, have received recognition from the Southeastern Conference for their performances on the football field.


title: Vanderbilt Commodores football

text: Five Vanderbilt players have been awarded Most Valuable Player, with three of them being awarded over a six year span to Commodores.


title: Wood ash

text: Wood ash is the powdery residue remaining after the combustion of wood, such as burning wood in a fireplace, bonfire, or an industrial power plant. It is largely composed of calcium compounds along with other non-combustible trace elements present in the wood. It has been used for many purposes throughout history.


title: Wood ash

text: A comprehensive set of analyses of wood ash composition from many tree species has been carried out by Emil Wolff, among others. Several factors have a major impact on the composition:


title: Wood ash

text: According to one research on the average the burning of wood results in about 6–10% ashes. The residue ash of 0.43 and 1.82 percent of the original mass of burned wood (dry basis) is produced for certain woods if it is pyrolized until all volatiles disappear and it is burned at for 8 hours. Also the conditions of the combustion affect the composition and amount of the residue ash, thus higher temperature will reduce the ash yield.


title: Wood ash

text: As the wood burns, it produces different compounds depending on a temperature used. Some studies cite calcium carbonate () as the major constituent, others find no carbonate at all but calcium oxide () instead. The latter is produced at higher temperatures (see calcination). The equilibrium reaction CaCO → CO + CaO has its equilibrium shifted leftward at and high partial pressure (such as in a wood fire) but shifted rightward at or when partial pressure is reduced.


title: Wood ash

text: Much of wood ash contains calcium carbonate (CaCO) as its major component, representing 25% or even 45% of total ash weight. At CaCO and KCO were identified in one case. Less than 10% (ten percent) is potash, and less than 1% (one percent) is phosphate.


title: Wood ash

text: There are trace elements of iron (Fe), manganese (Mn), zinc (Zn), copper (Cu) and some heavy metals. Their concentrations in ash vary due combustion temperature. Decomposition of carbonates and the volatilization of potassium (K), sulfur (S), and trace amounts of copper (Cu) and boron (B) may result from increased temperature. The study has found that at raised temperature K, S, B, sodium (Na) and copper (Cu) decreased, whereas Mg, P, Mn, Al, Fe, and Si did not change relative to calcium (Ca). All of these trace elements are, however, present in the form of oxides at higher temperature of combustion. Some elements in wood ash (all fractions given in mass/mass) include:


title: Wood ash

text: One study has determined that a slowly burning wood ( ) emissions typically include 16 alkenes, 5 alkadienes, 5 alkynes and several alkanes and arenes in proportions. Ethene, acetylene and benzene were a major part at efficient combustion. Proportion of C-C alkenes were found to be higher for smouldering. Benzene and 1,3-butadiene constituted ~10–20% and ~1–2% by weight of total non-methane hydrocarbons.


title: Wood ash

text: Wood ash can be used as a fertilizer used to enrich agricultural soil nutrition. In this role, wood ash serves as a source of potassium and calcium carbonate, the latter acting as a liming agent to neutralize acidic soils.


title: Wood ash

text: Wood ash can also be used as an amendment for organic hydroponic solutions, generally replacing inorganic compounds containing calcium, potassium, magnesium and phosphorus.


title: Wood ash

text: Wood ash is commonly disposed of in landfills, but with rising disposal costs, ecologically friendly alternatives, such as serving as compost for agricultural and forestry applications, are becoming more popular. Because wood ash has a high char content, it can be used as an odor control agent, especially in composting operations.


title: Wood ash

text: Wood ash has a very long history of being used in ceramic glazes, particularly in the Chinese, Japanese and Korean traditions, though now used by many craft potters. It acts as a flux, reducing the melting point of the glaze.


title: Wood ash

text: Potassium hydroxide can be made directly from wood ash by leaching and filtering it by using water. The technique for the lye production described as following: the hardwood ash is loaded into a waterproof vessel with gravel and straw laid at the bottom, with a small hole for the water to drip out. The drippings are usually collected by using a separate vessel underneath of the primary one. The apparatus is usually left for several hours (overnight) to collect leachate. The water and ashes may be continuously added to make more yield. In this form, it is known as caustic potash or lye. Because of this property, wood ash has also traditionally been used to make wood-ash soap.


title: Wood ash

text: The ectomycorrhizal fungi "Suillus granulatus" and "Paxillus involutus" can release elements from wood ash.


title: Wood ash

text: Wood ash is sometimes used in the process of nixtamalization, where corn is soaked and cooked in an alkali solution to improve nutritional content and decrease risk of mycotoxins. The alkali solution has historically been made from wood ash lye.


title: Wood ash

text: An early leavened bread was baked as early as 6000 BC by the Sumerians by placing the bread on heated stones and covering it with hot ash. The minerals in the wood ash could have supplemented the nutritional content of the dough as it was baked. In present day, the amount of wood ash content in bread flour, as measured by the Chopin alveograph, is strictly regulated by France.


title: Holkar

text: The Holkar (Pronunciation: ]) dynasty was a Maratha clan of Dhangar origin in India. The Holkars were generals under Peshwa Baji Rao I, and later became Maharajas of Indore in Central India as an independent member of the Maratha Empire until 1818. Later, their kingdom became a princely state under the protectorate of British India.


title: Holkar

text: The dynasty was founded with Malhar Rao, who joined the service of the Peshwas of the Maratha Empire in 1721, and quickly rose to the rank of Subedar. The name of the dynasty was associated with the title of the ruler, who was known informally as "Holkar Maharaja".


title: Holkar

text: Malhar Rao Holkar (1694–1766), a Maratha chief serving Peshwa Baji Rao, established the dynasty's rule over Indore. In the 1720s, he led Maratha armies in Malwa region, and in 1733 was granted 9 parghanas in the vicinity of Indore by the Peshwa. The township of Indore had already existed as an independent principality established by Nandlal Mandloi of Kampel, Nandlal Mandloi was won by the Maratha force and allowed them to camp across the Khan River. In 1734, Malhar Rao established a camp later called Malharganj. In 1747, he started the construction of his royal palace, the Rajwada. By the time of his death, he ruled much of Malwa, and was acknowledged as one of the five virtually independent rulers of the Maratha Confederacy.


title: Holkar

text: He was succeeded by Ahilyabai Holkar (r. 1767–1795), his daughter-in-law. She was born in the Chaundi village in Maharashtra. She moved the capital to Maheshwar, south of Indore on the Narmada River. "Rani" Ahilyabai was a prolific builder and patron of Hindu temples in Maheshwar and Indore. She also built temples at sacred sites outside her kingdom, from Dwarka in Gujarat east to the Kashi Vishwanath Temple at Varanasi on the Ganges.


title: Holkar

text: The adopted son of Malhar Rao Holkar, Tukoji Rao Holkar (r. 1795–1797) briefly succeeded Rani Ahilyabai upon her death. Tukoji Rao had been a commander under Ahilyabai for her entire rule.


title: Holkar

text: His son Yashwantrao Holkar (r. 1797–1811) (also called as Jaswant Rao) succeeded him upon his death. He tried to free the Delhi Mughal Emperor Shah Alam II from the British in the unsuccessful Second Anglo-Maratha War. The grateful Shah Alam gave him the title of "Maharajadiraj Rajrajeshwar Alija Bahadur" in honor of his bravery.


title: Holkar

text: Attempts by Yashwantrao Holkar to unite the kings failed, and he was approached to sign a peace treaty with the British. The Treaty of Rajghat, signed late December 1805, recognised him as a sovereign king.


title: Holkar

text: In 1811, the four-year-old Malhar Rao Holkar III succeeded Yashwantrao Holkar. His mother, Tulsabai Holkar, looked after the administration. However, with the help of Pathans, Pindaris, and the British, Dharama Kunwar and Balaram Seth plotted to imprison Tulsabai and Malharrao. When Tulsabai learnt about this, she beheaded both of them in 1815 and appointed Tantia Jog. As a result, Gaffur Khan Pindari secretly signed a treaty with the British on 9 November 1817 and killed Tulsabai on 19 December 1817.


title: Holkar

text: The treaty was signed on 6 January 1818 at Mandsaur. Bhimabai Holkar did not accept the treaty, and kept attacking the British by guerilla methods. Years later, in revolt of 1857, Rani Lakshmibai of Jhansi took inspiration from Bhimabai Holkar and also fought against the British. At the conclusion of the Third Anglo-Maratha War, the Holkars lost much of their territory to the British and were incorporated into the British Raj as a princely state of the Central India Agency. The capital was shifted from Bhanpura to Indore.


title: Holkar

text: Malharrao Holkar III entered Indore on 2 November 1818. Tantia Jog was appointed his Diwan as he was a minor. As the old palace had been destroyed by the army of Daulat Rao Scindia, a new palace was constructed in its place. Malharrao III was succeeded by Marthand Rao Holkar, who formally ascended to the throne on 17 January 1834. He was replaced by Hari Rao Holkar, nephew of Yashwantrao, who ascended to the throne on 17 April 1834. He adopted Khande Rao Holkar II on 2 July 1841 and died on 24 October 1843. Khanderao was formally installed as the ruler on 13 November 1843, but he suddenly died on 17 February 1844. Tukojirao Holkar II (1835–1886) was installed on the throne on 27 June 1844. During the Indian Rebellion of 1857, he was loyal to the British East India Company. In October 1872, he appointed T. Madhava Rao as the "Diwan" of Indore. He died on 17 June 1886 and was succeeded by his eldest son, Shivajirao Holkar.


title: Holkar

text: Yashwantrao Holkar II (reigned 1926–1948) ruled Indore state until shortly after India's independence in 1947, when he acceded to the Union of India. Indore became a district of Madhya Bharat state, which was merged into Madhya Pradesh state in 1956.


title: Holkar

text: On 22 April 1948 the Maharaja of Indore signed a covenant with the rulers of the adjoining princely states to form a new state known as Madhya Bharat. Madhya Bharat was created on 28 May 1948. On 16 June 1948, the princely state of Indore, which the House of Holkar ruled, merged with the newly independent Indian states.


title: Made in America (1993 film)

text: Made in America is a 1993 American comedy film starring Whoopi Goldberg and Ted Danson, and featuring Nia Long, Jennifer Tilly and Will Smith. The film was directed by Richard Benjamin. It was shot in various locations in Oakland, California and at Oakland Technical High School.


title: Made in America (1993 film)

text: "Colors of Love," written by Carole Bayer Sager, James Ingram and Bruce Roberts, and produced by David Foster is a notable song on the soundtrack which alludes to the story line.


title: Made in America (1993 film)

text: Zora Matthews takes a blood test and discovers she has a blood group which is not a possible combination of the groups of her deceased father and her mother Sarah (Whoopi Goldberg). She confronts her mother and is told she used a sperm bank after her husband's death, but asked for a smart black guy.


title: Made in America (1993 film)

text: Zora's curiosity gets the better of her and she gets her best friend Tea Cake (Will Smith) to go to the sperm bank and pretend to be a donor while she sneaks in to look at the records. Zora gets into the computer and the records show her mother was matched with a Halbert Jackson and gives his social security number.


title: Made in America (1993 film)

text: In his luxury mansion, Hal finishes a night of lovemaking with his young girlfriend Stacy before setting off to work. As he reverses out of the garage, Zora gets in the way. She gets in his pickup truck and he goes to his work: Jackson's Motors. She is shocked that he is white. Hal is a flamboyant salesman of his own premises and is shooting an advert with a real bear. Zora interrupts and tells him he is her father. He does not behave well and shows no sympathy or empathy.


title: Made in America (1993 film)

text: Her mom goes to confront Hal about how nasty he was. Zora and Tea Cake follow, but Tea Cake is diverted by the car saleswoman. Sarah and Hal are very different in every way. Hal plies her with drink and she cycles off in a drunken state. Hal drives her to her shop "African Queen" with the cycle in the back of the truck. The shop specializes in black authors. Upstairs is a shrine-like room with many pictures of Sarah growing up, plus a dead black man, Charles, which Sarah had been told was her father (photos of O. J. Simpson).


title: Made in America (1993 film)

text: Back in his mansion Hal gets emotional in regard to a father-daughter scene in a film on TV: Shirley Temple in "The Little Princess".


title: Made in America (1993 film)

text: With his penchant for hiring wild animals Hal next hires an Indian elephant but while riding it, the elephant charges off through the streets, ending in a pond. It has been following the sound of the bell on Sarah's bike. Hal takes out his anger on her bike and rips the bell off. News coverage of Hal and the elephant accidentally gives free publicity to his dealership and he has a sales bonanza on the following day.


title: Made in America (1993 film)

text: In apology, Hal later goes to her house with a present: a new bell plus a cycling helmet. He also has a present for Zora: an atlas to encourage her world travel. His assistant Diego arrives to take Zora on a date. This inspires Hal to ask Sarah out for dinner and they go for sushi. Hal isn't used to sushi and eats a whole ball of wasabi. Hal starts flirting heavily and they kiss goodnight. It gets more passionate but just as they get to the bedroom Zora and Diego get home. Zora is upset since she wanted to find her father but doesn't want her mom involved.


title: Made in America (1993 film)

text: Both Hal and Sarah start thinking about each other. When Sarah goes to the dealership, they argue and Sarah cycles off in an emotional state. She gets hit by a car when she runs a red light. She is not wearing her new helmet and is badly injured. Hal and Zora go to the hospital and give blood. They sit by her bedside as she recovers.


title: Made in America (1993 film)

text: When he goes back to the hospital a medic inadvertently reveals that a blood test shows that he cannot be Zora's father. Zora runs off. He apologises to Sarah.


title: Made in America (1993 film)

text: Zora and Tea Cake graduate from school. Zora wins a Westinghouse scholarship to MIT. At her speech Zora gives her mother all the credit. Hal arrives. He wants to see his daughter graduate. He helps Sarah up the steps and Zora thanks her Mom and Dad.


title: Made in America (1993 film)

text: The story did not originally specify black actors for any of the roles and was rewritten upon Goldberg's casting.


title: Made in America (1993 film)

text: The character of Hal Jackson is based in part on the real life car dealership owner Cal Worthington. Hal's use of large circus animals in his car commercials are an homage to Cal's famous "My Dog Spot" ads, which were also filmed with live circus animals.


title: Made in America (1993 film)

text: The film opened in theaters on May 28, 1993, and grossed over $12 million on its opening weekend. It was released to over 2,000 theaters and grossed nearly $50 million in the U.S. alone. Worldwide, it earned over $100 million. This was television star and Grammy Award-winning rapper Will Smith's second supporting role in a movie and started his successful career as a major film actor.


title: Made in America (1993 film)

text: "Made in America" earned mostly negative reviews from critics, holding a 33% rating on Rotten Tomatoes based on 30 reviews. However, Roger Ebert praised Goldberg's acting in the film and said "This isn't a great movie, but it sure is a nice one."


title: Support vector machine

text: In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974). Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.


title: Support vector machine

text: In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the , implicitly mapping their inputs into high-dimensional feature spaces.


title: Support vector machine

text: When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, and then map new data to these formed groups. The support vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data.


title: Support vector machine

text: Suppose some given data points each belong to one of two classes, and the goal is to decide which class a "new" data point will be in. In the case of support vector machines, a data point is viewed as a formula_1-dimensional vector (a list of formula_1 numbers), and we want to know whether we can separate such points with a formula_3-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So we choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the "maximum-margin hyperplane" and the linear classifier it defines is known as a "maximum-margin classifier"; or equivalently, the "perceptron of optimal stability".


title: Support vector machine

text: More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class (so-called functional margin), since in general the larger the margin, the lower the generalization error of the classifier.


title: Support vector machine

text: Whereas the original problem may be stated in a finite-dimensional space, it often happens that the sets to discriminate are not linearly separable in that space. For this reason, it was proposed that the original finite-dimensional space be mapped into a much higher-dimensional space, presumably making the separation easier in that space. To keep the computational load reasonable, the mappings used by SVM schemes are designed to ensure that dot products of pairs of input data vectors may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function formula_4 selected to suit the problem. The hyperplanes in the higher-dimensional space are defined as the set of points whose dot product with a vector in that space is constant, where such a set of vectors is an orthogonal (and thus minimal) set of vectors that defines a hyperplane. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters formula_5 of images of feature vectors formula_6 that occur in the data base. With this choice of a hyperplane, the points formula_7 in the feature space that are mapped into the hyperplane are defined by the relation formula_8 Note that if formula_4 becomes small as formula_10 grows further away from formula_7, each term in the sum measures the degree of closeness of the test point formula_7 to the corresponding data base point formula_6. In this way, the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. Note the fact that the set of points formula_7 mapped into any hyperplane can be quite convoluted as a result, allowing much more complex discrimination between sets that are not convex at all in the original space.


title: Support vector machine

text: The original SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. In 1992, Bernhard Boser, Isabelle Guyon and Vladimir Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. The "soft margin" incarnation, as is commonly used in software packages, was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995.


title: Support vector machine

text: where the formula_17 are either 1 or −1, each indicating the class to which the point formula_18 belongs. Each formula_19 is a formula_1-dimensional real vector. We want to find the "maximum-margin hyperplane" that divides the group of points formula_19 for which formula_22 from the group of points for which formula_23, which is defined so that the distance between the hyperplane and the nearest point formula_19 from either group is maximized.


title: Support vector machine

text: where formula_27 is the (not necessarily normalized) normal vector to the hyperplane. This is much like Hesse normal form, except that formula_27 is not necessarily a unit vector. The parameter formula_29 determines the offset of the hyperplane from the origin along the normal vector formula_27.


title: Support vector machine

text: If the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible. The region bounded by these two hyperplanes is called the "margin", and the maximum-margin hyperplane is the hyperplane that lies halfway between them. With a normalized or standardized dataset, these hyperplanes can be described by the equations


title: Support vector machine

text: Geometrically, the distance between these two hyperplanes is formula_33, so to maximize the distance between the planes we want to minimize formula_34. The distance is computed using the distance from a point to a plane equation. We also have to prevent data points from falling into the margin, we add the following constraint: for each formula_35 either


title: Support vector machine

text: The formula_27 and formula_40 that solve this problem determine our classifier, formula_41 where formula_42 is the sign function.


title: Support vector machine

text: An important consequence of this geometric description is that the max-margin hyperplane is completely determined by those formula_19 that lie nearest to it. These formula_19 are called "support vectors".


title: Support vector machine

text: To extend SVM to cases in which the data are not linearly separable, the "hinge loss" function is helpful


title: Support vector machine

text: Note that formula_17 is the "i"-th target (i.e., in this case, 1 or −1), and formula_47 is the "i"-th output.


title: Support vector machine

text: This function is zero if the constraint in is satisfied, in other words, if formula_19 lies on the correct side of the margin. For data on the wrong side of the margin, the function's value is proportional to the distance from the margin.


title: Support vector machine

text: where the parameter formula_50 determines the trade-off between increasing the margin size and ensuring that the formula_19 lie on the correct side of the margin. By deconstructing the hinge loss, this optimization problem can be massaged into the following:


title: Support vector machine

text: Thus, for large values of formula_53, it will behave similar to the hard-margin SVM, if the input data are linearly classifiable, but will still learn if a classification rule is viable or not. (formula_54 is inversely related to formula_53, e.g. in "LIBSVM".)


title: Support vector machine

text: The original maximum-margin hyperplane algorithm proposed by Vapnik in 1963 constructed a linear classifier. However, in 1992, Bernhard Boser, Isabelle Guyon and Vladimir Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick (originally proposed by Aizerman et al.) to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a nonlinear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be nonlinear and the transformed space high-dimensional; although the classifier is a hyperplane in the transformed feature space, it may be nonlinear in the original input space.


title: Support vector machine

text: It is noteworthy that working in a higher-dimensional feature space increases the generalization error of support vector machines, although given enough samples the algorithm still performs well.


title: Support vector machine

text: The kernel is related to the transform formula_65 by the equation formula_66. The value is also in the transformed space, with formula_67. Dot products with for classification can again be computed by the kernel trick, i.e. formula_68.


title: Support vector machine

text: We focus on the soft-margin classifier since, as noted above, choosing a sufficiently small value for formula_54 yields the hard-margin classifier for linearly classifiable input data. The classical approach, which involves reducing to a quadratic programming problem, is detailed below. Then, more recent approaches such as sub-gradient descent and coordinate descent will be discussed.


title: Support vector machine

text: Minimizing can be rewritten as a constrained optimization problem with a differentiable objective function in the following way.


title: Support vector machine

text: For each formula_70 we introduce a variable formula_71. Note that formula_72 is the smallest nonnegative number satisfying formula_73


title: Support vector machine

text: This is called the "dual" problem. Since the dual maximization problem is a quadratic function of the formula_76 subject to linear constraints, it is efficiently solvable by quadratic programming algorithms.


title: Support vector machine

text: Moreover, formula_79 exactly when formula_80 lies on the correct side of the margin, and formula_81 when formula_80 lies on the margin's boundary. It follows that formula_27 can be written as a linear combination of the support vectors.


title: Support vector machine

text: The offset, formula_84, can be recovered by finding an formula_80 on the margin's boundary and solving


title: Support vector machine

text: Suppose now that we would like to learn a nonlinear classification rule which corresponds to a linear classification rule for the transformed data points formula_89 Moreover, we are given a kernel function formula_90 which satisfies formula_91.


title: Support vector machine

text: The coefficients formula_76 can be solved for using quadratic programming, as before. Again, we can find some index formula_97 such that formula_81, so that formula_99 lies on the boundary of the margin in the transformed space, and then solve


title: Support vector machine

text: Recent algorithms for finding the SVM classifier include sub-gradient descent and coordinate descent. Both techniques have proven to offer significant advantages over the traditional approach when dealing with large, sparse datasets—sub-gradient methods are especially efficient when there are many training examples, and coordinate descent when the dimension of the feature space is high.


title: Support vector machine

text: Note that formula_103 is a convex function of formula_27 and formula_40. As such, traditional gradient descent (or SGD) methods can be adapted, where instead of taking a step in the direction of the function's gradient, a step is taken in the direction of a vector selected from the function's sub-gradient. This approach has the advantage that, for certain implementations, the number of iterations does not scale with formula_15, the number of data points.


title: Support vector machine

text: For each formula_108, iteratively, the coefficient formula_76 is adjusted in the direction of formula_110. Then, the resulting vector of coefficients formula_111 is projected onto the nearest vector of coefficients that satisfies the given constraints. (Typically Euclidean distances are used.) The process is then repeated until a near-optimal vector of coefficients is obtained. The resulting algorithm is extremely fast in practice, although few performance guarantees have been proven.


title: Support vector machine

text: The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the "hinge loss". Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss. This perspective can provide further insight into how and why SVMs work, and allow us to better analyze their statistical properties.


title: Support vector machine

text: In supervised learning, one is given a set of training examples formula_112 with labels formula_113, and wishes to predict formula_114 given formula_115. To do so one forms a hypothesis, formula_103, such that formula_117 is a "good" approximation of formula_114. A "good" approximation is usually defined with the help of a "loss function," formula_119, which characterizes how bad formula_120 is as a prediction of formula_10. We would then like to choose a hypothesis that minimizes the "expected risk:"


title: Support vector machine

text: In most cases, we don't know the joint distribution of formula_123 outright. In these cases, a common strategy is to choose the hypothesis that minimizes the "empirical risk:"


title: Support vector machine

text: Under certain assumptions about the sequence of random variables formula_125 (for example, that they are generated by a finite Markov process), if the set of hypotheses being considered is small enough, the minimizer of the empirical risk will closely approximate the minimizer of the expected risk as formula_15 grows large. This approach is called "empirical risk minimization," or ERM.


title: Support vector machine

text: In order for the minimization problem to have a well-defined solution, we have to place constraints on the set formula_127 of hypotheses being considered. If formula_127 is a normed space (as is the case for SVM), a particularly effective technique is to consider only those hypotheses formula_129 for which formula_130 . This is equivalent to imposing a "regularization penalty" formula_131, and solving the new optimization problem


title: Support vector machine

text: More generally, formula_133 can be some measure of the complexity of the hypothesis formula_103, so that simpler hypotheses are preferred.


title: Support vector machine

text: Recall that the (soft-margin) SVM classifier formula_135 is chosen to minimize the following expression:


title: Support vector machine

text: In light of the above discussion, we see that the SVM technique is equivalent to empirical risk minimization with Tikhonov regularization, where in this case the loss function is the hinge loss


title: Support vector machine

text: From this perspective, SVM is closely related to other fundamental classification algorithms such as regularized least-squares and logistic regression. The difference between the three lies in the choice of loss function: regularized least-squares amounts to empirical risk minimization with the square-loss, formula_138; logistic regression employs the log-loss,


title: Support vector machine

text: The difference between the hinge loss and these other loss functions is best stated in terms of "target functions -" the function that minimizes expected risk for a given pair of random variables formula_140.


title: Support vector machine

text: In particular, let formula_141 denote formula_10 conditional on the event that formula_143. In the classification setting, we have:


title: Support vector machine

text: For the square-loss, the target function is the conditional expectation function, formula_146; For the logistic loss, it's the logit function, formula_147. While both of these target functions yield the correct classifier, as formula_148, they give us more information than we need. In fact, they give us enough information to completely describe the distribution of formula_149.


title: Support vector machine

text: On the other hand, one can check that the target function for the hinge loss is "exactly" formula_150. Thus, in a sufficiently rich hypothesis space—or equivalently, for an appropriately chosen kernel—the SVM classifier will converge to the simplest function (in terms of formula_151) that correctly classifies the data. This extends the geometric interpretation of SVM—for linear classification, the empirical risk is minimized by any function whose margins lie between the support vectors, and the simplest of these is the max-margin classifier.


title: Support vector machine

text: SVMs belong to a family of generalized linear classifiers and can be interpreted as an extension of the perceptron. They can also be considered a special case of Tikhonov regularization. A special property is that they simultaneously minimize the empirical "classification error" and maximize the "geometric margin"; hence they are also known as maximum margin classifiers.


title: Support vector machine

text: The effectiveness of SVM depends on the selection of kernel, the kernel's parameters, and soft margin parameter formula_54.


title: Support vector machine

text: A common choice is a Gaussian kernel, which has a single parameter "formula_153". The best combination of formula_54 and formula_153 is often selected by a grid search with exponentially growing sequences of formula_54 and "formula_153", for example, formula_158; formula_159. Typically, each combination of parameter choices is checked using cross validation, and the parameters with best cross-validation accuracy are picked. Alternatively, recent work in Bayesian optimization can be used to select formula_54 and "formula_153" , often requiring the evaluation of far fewer parameter combinations than grid search. The final model, which is used for testing and for classifying new data, is then trained on the whole training set using the selected parameters.


title: Support vector machine

text: SVC is a similar method that also builds on kernel functions but is appropriate for unsupervised learning.


title: Support vector machine

text: Multiclass SVM aims to assign labels to instances by using support vector machines, where the labels are drawn from a finite set of several elements.


title: Support vector machine

text: The dominant approach for doing so is to reduce the single multiclass problem into multiple binary classification problems. Common methods for such reduction include:


title: Support vector machine

text: Crammer and Singer proposed a multiclass SVM method which casts the multiclass classification problem into a single optimization problem, rather than decomposing it into multiple binary classification problems. See also Lee, Lin and Wahba and Van den Burg and Groenen.


title: Support vector machine

text: Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning by following the principles of transduction. Here, in addition to the training set formula_162, the learner is also given a set


title: Support vector machine

text: of test examples to be classified. Formally, a transductive support vector machine is defined by the following primal optimization problem:


title: Support vector machine

text: SVMs have been generalized to structured SVMs, where the label space is structured and of possibly infinite size.


title: Support vector machine

text: A version of SVM for regression was proposed in 1996 by Vladimir N. Vapnik, Harris Drucker, Christopher J. C. Burges, Linda Kaufman and Alexander J. Smola. This method is called support vector regression (SVR). The model produced by support vector classification (as described above) depends only on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin. Analogously, the model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction. Another SVM version known as least-squares support vector machine (LS-SVM) has been proposed by Suykens and Vandewalle.


title: Support vector machine

text: where formula_6 is a training sample with target value formula_17. The inner product plus intercept formula_174 is the prediction for that sample, and formula_175 is a free parameter that serves as a threshold: all predictions have to be within an formula_175 range of the true predictions. Slack variables are usually added into the above to allow for errors and to allow approximation in the case the above problem is infeasible.


title: Support vector machine

text: In 2011 it was shown by Polson and Scott that the SVM admits a Bayesian interpretation through the technique of data augmentation. In this approach the SVM is viewed as a graphical model (where the parameters are connected via probability distributions). This extended view allows the application of Bayesian techniques to SVMs, such as flexible feature modeling, automatic hyperparameter tuning, and predictive uncertainty quantification. Recently, a scalable version of the Bayesian SVM was developed by Florian Wenzel, enabling the application of Bayesian SVMs to big data. Florian Wenzel developed two different versions, a variational inference (VI) scheme for the Bayesian kernel support vector machine (SVM) and a stochastic version (SVI) for the linear Bayesian SVM.


title: Support vector machine

text: The parameters of the maximum-margin hyperplane are derived by solving the optimization. There exist several specialized algorithms for quickly solving the quadratic programming (QP) problem that arises from SVMs, mostly relying on heuristics for breaking the problem down into smaller, more manageable chunks.


title: Support vector machine

text: Another approach is to use an interior-point method that uses Newton-like iterations to find a solution of the Karush–Kuhn–Tucker conditions of the primal and dual problems.


title: Support vector machine

text: Instead of solving a sequence of broken-down problems, this approach directly solves the problem altogether. To avoid solving a linear system involving the large kernel matrix, a low-rank approximation to the matrix is often used in the kernel trick.


title: Support vector machine

text: Another common method is Platt's sequential minimal optimization (SMO) algorithm, which breaks the problem down into 2-dimensional sub-problems that are solved analytically, eliminating the need for a numerical optimization algorithm and matrix storage. This algorithm is conceptually simple, easy to implement, generally faster, and has better scaling properties for difficult SVM problems.


title: Support vector machine

text: The special case of linear support vector machines can be solved more efficiently by the same kind of algorithms used to optimize its close cousin, logistic regression; this class of algorithms includes sub-gradient descent (e.g., PEGASOS) and coordinate descent (e.g., LIBLINEAR). LIBLINEAR has some attractive training-time properties. Each convergence iteration takes time linear in the time taken to read the train data, and the iterations also have a Q-linear convergence property, making the algorithm extremely fast.


title: Support vector machine

text: The general kernel SVMs can also be solved more efficiently using sub-gradient descent (e.g. P-packSVM), especially when parallelization is allowed.


title: Support vector machine

text: Kernel SVMs are available in many machine-learning toolkits, including LIBSVM, MATLAB, SAS, SVMlight, kernlab, scikit-learn, Shogun, Weka, Shark, JKernelMachines, OpenCV and others.


title: Support vector machine

text: Preprocessing of data (standardization) is highly recommended to enhance accuracy of classification. There are a few methods of standardization, such as min-max, normalization by decimal scaling, Z-score. Subtraction of mean and division by variance of each feature is usually used for SVM.


title: Marvin Lewis

text: Marvin Ronald Lewis (born September 23, 1958) is an American football coach who is the special advisor to the head coach at Arizona State. Previously, Lewis was the head coach of the Cincinnati Bengals in the National Football League (NFL) for 16 seasons. He came to prominence as the defensive coordinator of the Baltimore Ravens from 1996 to 2001, whose defense in 2000 set the record for the fewest points allowed in a 16-game season and led the franchise to their first Super Bowl title in Super Bowl XXXV. This success resulted in Lewis being named the Bengals' head coach, where he served from 2003 to 2018. He was also a commentator for the Alliance of American Football (AAF).


title: Marvin Lewis

text: Lewis' head coaching tenure oversaw improved fortunes for the struggling Bengals and helped transform the team into postseason contenders. At the time of his hiring, the Bengals had not had a winning season or postseason appearance since 1990 and finished with a franchise-worst 2–14 record. Within his third season, Lewis ended both droughts and led the Bengals to their first division title in 15 years. In total, he guided the Bengals to seven playoff appearances and four division titles, along with a franchise-best five consecutive postseason appearances from 2011 to 2015. Lewis is the franchise's winningest head coach at 131 and was named Coach of the Year in 2009, making him the first Bengals coach to receive the honor since team founder Paul Brown in 1970.


title: Marvin Lewis

text: While credited with returning the Bengals to respectability, Lewis' reputation was affected by a lack of postseason success and he was ultimately unable to lead the team to a playoff win. His 131 regular season victories, 16 years as a head coach, and seven postseason losses are the most of NFL head coaches who have not won a playoff game.


title: Marvin Lewis

text: Marvin Lewis was born in McDonald, Pennsylvania, a small town about twenty miles west of Pittsburgh. He started playing football at the age of 9 and played safety and quarterback for his team at Fort Cherry High School. He was on the wrestling team and played baseball in the summers.


title: Marvin Lewis

text: He initially decided to walk on as a football player at Purdue University, but subsequently received a scholarship to attend Idaho State University. He primarily played linebacker. In 2001, he was inducted into Idaho State University's Sports Hall of Fame. He was named Idaho State Alumni of the Year for 2012. Lewis received both his bachelor's degree in physical education and a master's degree in athletic administration from Idaho State.


title: Marvin Lewis

text: Lewis began his coaching career as a graduate assistant at Idaho State before becoming the team's linebackers coach for four seasons (1981–1984). Idaho State won the NCAA Division I-AA Championship during his first year with the team.


title: Marvin Lewis

text: Lewis was an assistant coach at Long Beach State University (1985–1986), the University of New Mexico (1987–1989), and the University of Pittsburgh (1990–1992).


title: Marvin Lewis

text: Lewis had coaching internships with the Kansas City Chiefs and the San Francisco 49ers before being hired as the linebackers coach for the Pittsburgh Steelers in 1992. He was a member of the Steelers' staff when the team lost Super Bowl XXX to the Dallas Cowboys.


title: Marvin Lewis

text: The newly relocated Baltimore Ravens (formerly the Cleveland Browns), hired Lewis as their defensive coordinator in 1996, a position that he held for six seasons (1996–2001). On January 28, 2001, the Ravens defeated the New York Giants 34–7 in Super Bowl XXXV thanks largely to a defense that allowed the fewest rushing yards (970) and the fewest points (165) in a 16-game regular season. "If ever a man proved his worth as a future head coach, Marvin Lewis did it with this complete domination of the Giants in their 16 possessions: Punt, punt, punt, punt, punt, punt, interception, punt, interception, interception, punt, interception, punt, punt, punt, end of game", wrote "Sports Illustrated" writer Michael Silver after the game.


title: Marvin Lewis

text: Lewis was a prime candidate for several NFL head coaching jobs but was passed over each time. Most notably, he nearly became head coach of the Tampa Bay Buccaneers in 2002. General manager Rich McKay was ready to formally offer the job to Lewis, and the Ravens actually held a going-away party for him. However, the team's owners, the Glazer family, were unwilling to give the job to another defense-minded coach after firing Tony Dungy. Lewis was also a prime candidate for the Buffalo Bills coaching vacancy, but was passed over in favor of Tennessee Titans defensive coordinator Gregg Williams. Shortly afterward, Lewis was hired by the Washington Redskins as defensive coordinator and assistant head coach under Steve Spurrier.


title: Marvin Lewis

text: Lewis became the ninth coach in Cincinnati Bengals history on January 14, 2003, when he was hired to replace Dick LeBeau, who was fired after the worst season in franchise history in terms of win percentage, edging out Tom Coughlin and Mike Mularkey. Lewis also had interviews with the Buffalo Bills, the Carolina Panthers, Tampa Bay Buccaneers, and the Cleveland Browns. Lewis also declined head coaching positions in the college ranks with the University of California, Berkeley and Michigan State University to continue pursuing his goal of becoming a head coach in the NFL.


title: Marvin Lewis

text: A contending team in the mid-late 1970s through the 1980s, the Bengals had fallen on hard times in the 1990s and had had several head coaches. However, Lewis didn't take long to return the Bengals to respectability with a nucleus of young players such as quarterback Carson Palmer, running back Rudi Johnson, receivers Chad Johnson and T. J. Houshmandzadeh, and defensive backs Tory James and Deltha O'Neal. After consecutive 8–8 seasons, Lewis led the Bengals to an 11–5 record in 2005, their first winning season, first division title, and first playoff berth in 15 years. However, the Bengals lost in the first round to the eventual Super Bowl XL champion Pittsburgh Steelers with Steelers' Kimo von Oelhoffen diving into Palmer's knee during the first play, forcing Palmer out of the game and requiring reconstructive surgery during the off season.


title: Marvin Lewis

text: The Bengals dropped to 8–8 in 2006, a disappointing season in which they started out 8–5 and then lost their last three games of the season, any one of which could have gotten them into the playoffs with a win. Then they recorded two consecutive losing seasons, including a 4–11–1 record in 2008. But in 2009, Cincinnati recorded their second winning season under Lewis' tenure. This included wins in all six games against their AFC North opponents, marking the first time in franchise history they accomplished this feat. The Bengals finished the season 10–6, winning the AFC North title and earning only their second trip to the playoffs in 19 years. On January 9, 2010, The Bengals were defeated by the New York Jets 24–14 in the opening round of the playoffs. On January 16, 2010, Lewis was named the Associated Press 2009 NFL coach of the year, after the Bengals improved from a 4–11–1 record in 2008 to a 10–6 regular season record in 2009.


title: Marvin Lewis

text: The Bengals slipped to a 4–12 record in 2010, the worst record of Lewis' Bengals coaching tenure and the first time that the team finished last in the AFC North with Lewis as their head coach.


title: Marvin Lewis

text: On January 4, 2011, Lewis signed an extension with the Bengals. The off-season leading up to 2011 was a difficult time for the Bengals. The team lost three of their most productive players from the 2010 season, receivers Terrell Owens and Chad Ochocinco along with defensive back Johnathan Joseph, while quarterback Carson Palmer, the team's starter since 2004, refused to play for the Bengals moving forward, leading to him being traded midway through the season.


title: Marvin Lewis

text: However, with the aid of strong play from their first and second-round draft picks, receiver A. J. Green and quarterback Andy Dalton, the Bengals still managed to record their third winning season. Midway through 2011, Lewis won his 65th game with the Bengals, surpassing Sam Wyche as the winningest coach in Bengals history. By the halfway mark, the Bengals' record was 6–2, including a five-game winning streak. It was the first time the Bengals had won five consecutive games since 1988, when the team advanced to the Super Bowl with Wyche as their coach. They finished the season 9–7 and made the playoffs as the #6 seed, where the Bengals lost to the Houston Texans in the wild card round.


title: Marvin Lewis

text: On July 31, 2012, the Bengals gave Lewis a two-year contract extension through 2014. Cincinnati started 2012 with a 44–13 loss to the Baltimore Ravens, the most lopsided opening day defeat in franchise history. But the team recovered and won their next three games. After defeating the Steelers in week 16, the Bengals again clinched the #6 seed in the AFC and eliminated the Steelers from playoff contention. This marked the first time the Bengals made the playoffs in consecutive seasons since 1982, ending the longest streak of failure to make consecutive playoff appearances among all 32 NFL teams. Cincinnati finished the season with a 10–6 record, including a franchise record 51 quarterback sacks. However, the season again concluded with a loss in the wild card round handed to them by the Texans.


title: Marvin Lewis

text: The 2013 season was one of the most successful in Lewis's career as head coach. Cincinnati finished with an 11–5 record and won their third division title since 2002. They were upset in the first round of the playoffs by the 9–7 San Diego Chargers, a team they had beaten earlier in the season 20–13. It was the third consecutive season that ended in a wild card round playoff loss for Cincinnati.


title: Marvin Lewis

text: By 2014, Lewis acquired considerable authority over football operations. Owner Mike Brown was still reckoned as the team's de facto general manager and retains the final say on football matters, but ceded most authority over day-to-day personnel matters to Lewis.


title: Marvin Lewis

text: In 2014, Lewis became the 37th coach in NFL history to record 100 regular season wins. The Bengals continued their playoff streak, posting a 10–5–1 record. They held the fifth seed in the AFC playoffs and drew the 11–5 Indianapolis Colts as their first-round opponent. In week 7, the Bengals had been shut out 27–0 in Indianapolis. They lost again 26–13, despite having 13–10 lead at halftime. With the Bengals' defeat, Lewis tied Jim E. Mora for the most postseason losses as a head coach without a win. There was some speculation that Lewis's head coaching job was on thin ice after a fourth consecutive first-round playoff exit, but on April 22, 2015, Lewis signed an extension with the Bengals through 2016.


title: Marvin Lewis

text: Cincinnati started out the 2015 season with an 8–0 record, the best start in franchise history. The team finished the year 12–4, the best record of Lewis' head coaching tenure and marking only the third time the Bengals had ever recorded 12 wins. However, the team was eliminated in a first-round wild card game for the fifth straight year, this time against divisional rival Pittsburgh Steelers. Leading 16–15 near the end of the fourth quarter, Lewis faced criticism for not keeping his players under control after penalties drawn by Vontaze Burfict and Adam Jones moved the Steelers into field goal range and allowed them to make a game-winning kick with eighteen seconds remaining. The defeat made Lewis the first NFL coach to lose seven postseason games without any wins and the Bengals the first NFL team to lose five straight playoff games in the opening round.


title: Marvin Lewis

text: The 2016 and 2017 seasons marked a noted decline for the Bengals, who finished 6-9-1 in the former and 7–9 in the latter to fail to qualify the postseason for two consecutive seasons. The seasons marked Lewis' first losing records with the team since 2010 and first consecutive losing years since 2008, as well as the first losing seasons and missed playoff appearances after the acquisitions of quarterback Andy Dalton and wide receiver A. J. Green.


title: Marvin Lewis

text: The losing seasons, combined with Lewis' winless playoff record and previous five consecutive first-round eliminations led to speculation towards his future in Cincinnati and the potential end of his coaching tenure. This speculation was fueled by a report during the 2017 season that said Lewis was planning to leave the Bengals after his contract expired at the end of the season to pursue other opportunities. Following the conclusion of the 2017 regular season, the report was disproved when Lewis signed a two-year contract extension. The extension was met with criticism from the media and Bengals fans due to his 0–7 playoff record and only winning 13 out of 32 games in the previous two seasons.


title: Marvin Lewis

text: Despite the criticism towards Lewis' return, the Bengals had a strong start in 2018, leading the AFC North with a 4–1 record by Week 5 and holding a 5–3 record before their bye week. After the bye, the team suffered a collapse and lost five consecutive games. During the losing streak, Lewis assumed control of the defense when defensive coordinator Teryl Austin was fired. Afflicted by season-ending injuries to several players and a struggling defense that ranked last in yards allowed, the Bengals finished the year with a 6–10 record to place at the bottom of their division. The season marked the first time since 2008 that the team failed to qualify for the playoffs in three consecutive years and the first time that Lewis had three consecutive seasons with sub-.500 records.


title: Marvin Lewis

text: With a year left on his contract, Lewis and the Bengals announced that they had mutually parted ways following the conclusion of the 2018 season on December 31. Lewis finished as the first Bengals head coach to leave the team with an overall winning record since Bill Johnson in 1978.


title: Marvin Lewis

text: Lewis was hired as a special advisor at Arizona State University on May 28, 2019. On February 4, 2020, he was promoted to co-defensive coordinator alongside Antonio Pierce.


title: Marvin Lewis

text: After his departure from the Bengals, Lewis was hired as a game and studio analyst for Turner Sports's coverage of the Alliance of American Football (AAF).


title: Marvin Lewis

text: Lewis is married and has a daughter and a son. His son Marcus is the running backs coach at Hampton University after serving as the Bengals' defensive line and quality control coach.


title: Greater Montreal

text: Greater Montreal () is the most populous metropolitan area in Quebec and the second most populous in Canada after Greater Toronto. In 2015, Statistics Canada identified Montreal's Census Metropolitan Area (CMA) as with a population of 4,027,100, almost half that of the province.


title: Greater Montreal

text: A smaller area of is governed by the Montreal Metropolitan Community (MMC) (, CMM). This level of government is headed by a president (currently Montreal mayor Valérie Plante).


title: Greater Montreal

text: The inner ring is composed of densely populated municipalities located in close proximity to Downtown Montreal. It includes the entire Island of Montreal, Laval, and the Urban Agglomeration of Longueuil. Due to their proximity to Montreal's downtown core, some additional suburbs on the South Shore (Brossard, Saint-Lambert, and Boucherville) are usually included in the inner ring, despite their location on the mainland.


title: Greater Montreal

text: The outer ring is composed of low-density municipalities located on the fringe of Metropolitan Montreal. Most of these cities and towns are semi-rural. Specifically, the term "off-island suburbs" refers to those suburbs that are located on the North Shore of the Mille-Îles River, those on the South Shore that were never included in the megacity of Longueuil, and those on the Vaudreuil-Soulanges Peninsula.


title: Greater Montreal

text: Only a portion of the municipalities and MRC's located in geographical entities highlighted in light gray are part of the CMM/CMA.<br>


title: Greater Montreal

text: There are 82 municipalities that are part of the MMC and 91 municipalities that are part of the CMA. <br>


title: Greater Montreal

text: There are 79 municipalities that overlap between the two, with 3 municipalities being part of the MMC but not the CMA, and 12 municipalities being part of the CMA but not the MMC.<br>


title: Greater Montreal

text: Exo operates the region's commuter rail and metropolitan bus services, and is the second busiest such system in Canada after Toronto's GO Transit. Established in June 2007, Exo's commuter rail system has six lines linking the downtown core with communities as far west as Hudson, as Far south as Mont-Saint-Hilaire, as far east as Mascouche, and as far north as Saint-Jérôme.


title: Greater Montreal

text: Along with Exo, a sister agency, the Autorité régionale de transport métropolitain (ARTM) plans, integrates, and coordinates public transport across Greater Montreal, including the Island of Montreal, Laval (Île Jésus), and communities along both the north shore of the Rivière des Mille-Îles and the south shore of the Saint Lawrence River. The ARTM's mandate also includes the management of reserved High-occupancy vehicle lanes, metropolitan bus terminuses, park-and-ride lots, and a budget of $163 million, which is shared amongst the transit corporations and inter-municipal public transit organizations.


title: Greater Montreal

text: The Exo/ARTM's territory spans 63 municipalities and one native reserve, 13 regional county municipalities, and 21 transit authorities. It serves a population of approximately 3.7 million people who make more than 750,000 trips daily.


title: Narappa

text: Narappa is a 2021 Indian Telugu-language period action drama film directed by Srikanth Addala. It is remake of Vetrimaaran's Tamil-language film "Asuran" (2019) which is itself based on the novel "Vekkai" by Poomani. The film is produced by D. Suresh Babu and Kalaipuli S. Thanu under their respective banners Suresh Productions and V Creations. The film stars Venkatesh, Priyamani, Karthik Rathnam, and Rakhi, while Nassar, Rao Ramesh, Rajeev Kanakala, Brahmaji, Ammu Abhirami, and Karthik Rathnam plays the other important supporting roles.


title: Narappa

text: Mani Sharma composed the film's music while cinematography and editing are performed by Shyam K. Naidu and Marthand K. Venkatesh respectively. Principal photography of the film started in January 2020 and was shot in Andhra Pradesh, Tamil Nadu and Telangana. "Narappa" premiered on 20 July 2021 on Amazon Prime Video.


title: Narappa

text: In the 1980s Anantapur district, a man and a boy are trekking across a river, carrying homemade bombs. In another part of the village, a woman, a man, and a young girl are also hiding from the police.


title: Narappa

text: The narrator reveals in a flashback that the man is Narappa and the boy is his son Seenappa. The woman, man, and child are Narappa's wife, Sundaramma, her brother Basavaiah and daughter Bujjamma. They also had an older son called Munikanna. They are a family of farmers living in Ramasagaram. Narappa has a close relationship with his older son but is distant, almost abusive with Seenappa. Narappa is from a family of farmers.


title: Narappa

text: Panduswamy is a landlord from Vadakoor (lit. northern village), who plans a cement factory with his youngest son Doraswamy and they need Narappa's 3 acres of land, which the family is against selling, especially Munikanna. One day Sundaramma is attacked by Panduswamy's son Rangababu, to which Munikanna retaliates by severely injuring him and gets imprisoned. Consequently, Narappa pleads with Panduswamy to get Munikanna released, and he asks Narappa to prostrate before every male resident of the landlord's village as atonement. Being cognizant of it, Munikanna beats Panduswamy with a slipper. As revenge, Panduswamy's henchmen, led by a hunter Gampanna, behead Munikanna. Sundaramma is heartbroken, and after a year, Seenappa is compelled to kill Panduswamy in revenge. Narappa witnesses the attack and flees with his family.


title: Narappa

text: The film returns to the present. Gampanna is ordered by Doraswamy & Rangababu to find the pair. He tracks them down and almost kills Seenappa. Narappa defeats them all and when they recover, Narappa explains why he spared their lives.


title: Narappa

text: Twenty years earlier, he is a village-famous moonshine brewer and trusted servant to Shankaraiah, a landlord. One day, he convinces his employer to employ Seenu, Shankaraiah's distant relative, as a clerk. Narappa's brother Munnappa and Varadarajulu, a lawyer, are attempting to regain their poor farming community's land from landlords who had seized it illegally. Narappa's family repeatedly fails to find a bride for him as he is a brewer. Frustrated Narappa's elder sister's daughter Kannamma, proposes to her uncle which he and the family accepts. Seenu slowly gains Shankaraiah's trust and becomes his mill accountant. Once Narappa beats Seenu inside the mill, for refusing to loan Rs. 1 to him and he plans revenge. Meanwhile, Narappa gifts sandals to Kannamma. Noticing Kannamma wearing sandals, Seenu hits her and forces her to walk through the village with the sandals on her head. Narappa retaliates by hitting him with sandals in the middle of the village and ties him up but is reprimanded by Shankaraiah. Humiliated Narapppa quits brewing and joins his brother. One night, before a meeting is conducted by Munnappa and Narappa, the news arrives that Varadarajulu is arrested, and Narappa is sent to get the documents authorizing the meeting. When he returns, Munappa has been killed by Seenu and Narappa's entire family is burned alive in their huts along with Kannamma. Enraged, Narappa brutally kills Shankaraiah, Seenu, and their men.


title: Narappa

text: After that, Narappa leaves the village and months later is found by Basavaiah, who taught him farming. He's also introduced to Sundaramma, who is not getting marriage proposals. Then, he narrates his story, and Sundaramma admires what he did for his family. He surrenders to the court and is given a short-term penalty. After serving his sentence he marries Sundaramma. Seenappa is amazed at his father's past and starts to admire him. Narappa reconciles with his wife and reaches Varadarajulu for help who advises them to turn up to court the next day.


title: Narappa

text: Doraswamy and Rangababu plan an ambush near the court premises and Narappa and his son flee. Later Narappa reluctantly sells his land to protect Seenappa from Panduswamy's family. However, Seenappa is abducted and tortured by Doraswamy & Rangababu, in direct violation of the agreement. At this point, the infuriated Narappa kills them and many of their henchmen. He and Seenappa sustain serious injuries. Basavaiah and their villagers arrive armed and defuse the situation.


title: Narappa

text: In the end, it is revealed that the two villages agreed that the conflict should end and prevent a village clash. Narappa's family has gathered in the court, and he has agreed to go to jail instead of Seenappa if necessary. He tells his son to study well and get a job, and that, unlike land and money, the landlords can't seize their education. The film ends with Narappa smiling at his family as he enters the court.


title: Narappa

text: In November 2019, Srikanth Addala confirmed that he would be directing Telugu remake of "Asuran" with Venkatesh playing the titular role. Title was revealed as "Narappa" in January 2020. The film marks the second collaboration of Venkatesh and Srikanth Addala after working in the 2013 family drama film "Seethamma Vakitlo Sirimalle Chettu". Venkatesh plays the role of Narappa, belonging to an oppressed caste from Anantapuram, Andhra Pradesh. Similar to "Asuran", Venkatesh plays a man of his mid-40s. Priyamani was cast as Sundaramma and Karthik Rathnam as Munikanna, playing Narappa's wife and elder son respectively.


title: Narappa

text: Principal photography of the film began on 22 January 2020 at Paalturu village near Uravakonda in Anantapur district. First schedule was to be shot in Rayalaseema region for authenticity. The film team then moved to Tamil Nadu to film few action scenes, and completed it in the March 2020, before the COVID-19 lockdown in India. These scenes were shot under the supervision of action director Peter Hein at Kurumalai, Theri Kaadu (Teri dune complex), Tirunelveli and other areas of Tamil Nadu. Filming has been suspended in March 2020 due to COVID-19 pandemic. It was reported that the film's shooting will be resumed in October 2020. Later, it was confirmed that the filming will be resumed on 9 November 2020 in Hyderabad, with Venkatesh being part of the filming for five days. By the time, the film has completed 80% of the shooting. By the end of January 2021, the shooting was wrapped up. Venkatesh completed his part of dubbing in May 2021, during the second-wave lockdown.


title: Narappa

text: Sangeetha Devi Dundoo felt that "Narappa" is a story of revenge and the leading man's transformation is reminiscent of Rajinikanth in "Baashha" (1995). The film explores various social issues in India, including caste discrimination and primarily shows the theme of "rich vs poor". The core plot of the film is influenced by the real-life Kilvenmani massacre that occurred in 1968 in Kizhavenmani village, Tamil Nadu.


title: Narappa

text: After working as the film scorer in the film "Seethamma Vakitlo Sirimalle Chettu" (2013), this is the second time Mani Sharma, collaborating with Srikanth, and first time as the composer of soundtrack album. First single "Chalaaki Chinnammi" was released on 11 July 2021 through Suresh Productions Music label. The second single "Ooo Narappa" was released on 16 July 2021. Third single "Rage of Narappa" was released on 23 July 2021. The soundtrack album (jukebox) was released on 30 July 2021.


title: Narappa

text: The film was scheduled to release on 14 May 2021 but was postponed due to the second wave of the COVID-19 pandemic in India. On 29 June 2021, it was announced that film received U/A certification from Central Board of Film Certification. In July 2021, it was announced that the film would have its digital premiere on Amazon Prime Video on 20 July 2021, forgoing its theatrical release. About the film's release on Amazon Prime Video, producer Suresh Babu commented that "The film's core message is very powerful. It does not shy away from the truths. I am certain that it will impact in ways that no one would expect. I am incredibly happy that we are able to showcase our film to audiences across 240 countries and territories".


title: Narappa

text: Neeshita Nyayapati of "The Times of India" gave a rating of 3.5 out of 5 and wrote that "In this adaptation of Poomani’s acclaimed novel "Vekkai", Srikanth Addala gets out of the comfort zone of his usually bright and happy family dramas." Writing for "Cinema Express", Ram Venkat Srikar commented that "In all, "Narappa" is as faithful a remake can get; it is, perhaps, too immaculate for its own good. When the original is a film like "Asuran", there is no way a remake can go wrong, and fittingly, "Narappa", despite its shortcomings, is a welcome change in Telugu cinema."


title: Narappa

text: Baradwaj Rangan of "Film Companion" stated that "The screenwriting in the film is cyclical: what happens in one generation finds an echo in the next. "Narappa" isn’t quite as good as "Asuran" but — on its own terms — it’s pretty good."