Friday, August 31, 2007

William Harrison "Bill" Frist, Sr. (born February 22, 1952) is an American physician, businessman, and politician. He is a former United States Senator from Tennessee. Frist was also Senate Majority Leader. Frist is a Republican and was frequently mentioned as a candidate for that party's 2008 presidential nomination, but decided in November 2006 not to run.

Childhood and medical career
Though he was a public policy major in college, Frist was late to take an interest in politics; he did not vote for the first time until he was 36.

Entering politics
Frist first entered the national spotlight when two Capitol police officers were shot inside the United States Capitol by Russell Eugene Weston Jr. in 1998. Frist, the closest doctor, provided immediate medical attention (he was unable to save the two officers, but was able to save Weston). He also was the Congressional spokesman during the 2001 anthrax attacks.
As the chairman of the National Republican Senatorial Committee, he helped Republicans win back the Senate in the 2002 midterm election. His committee collected $66.4 million for 2001–2002, 50% more than the previous year. Shortly afterwards, Sen. Trent Lott made comments at a Strom Thurmond birthday celebration in which he said that if Thurmond's segregationist presidential bid of 1948 had succeeded, "we wouldn't have all these problems today". In the aftermath, Lott resigned his position as Senate Majority Leader and Frist was chosen unanimously by Senate Republicans as his replacement. He became the second youngest Senate Majority Leader in US history. In his 2005 book, "Herding Cats, A Lifetime in Politics", Lott accuses William Frist of being "one of the main manipulators" in the debate that ended Senator Lott's leadership in the Republican Senate. Lott wrote that Senator Frist's actions amounted to a "personal betrayal." Frist "...didn't even have the courtesy to call and tell me personally that he was going to run." "If Frist had not announced exactly when he did, as the fire was about to burn out, I would still be majority leader of the Senate today." Lott wrote.
In the 2003 legislative session, Frist enjoyed many successes. He was able to push many initiatives through to fruition, including the Bush administration's third major tax cut and legislation that was against partial-birth abortion. However, the tactics that he used to achieve those victories alienated many Democrats. In 2004, by comparison, he saw no major legislative successes, with the explanations ranging from delay tactics by Democrats to lack of unity within the Republican Party.
In a prominent and nationally broadcast speech to the Republican National Convention in August, 2004, Frist highlighted his background as a doctor and focused on several issues related to health care. He spoke in favor of the recently passed Medicare prescription drug benefit and the passage of legislation providing for Health Savings Accounts. He described President Bush's policy regarding stem cell research, limiting embryonic stems cells to certain existing lines, as "ethical." In an impassioned argument for medical malpractice tort reform, Frist called personal injury trial lawyers "predators": "We must stop them from twisting American medicine into a litigation lottery where they hit the jackpot and every patient ends up paying." Frist has been an advocate for imposing caps on the amount of money courts can award plaintiffs for noneconomic damages in medical malpractice cases. .

National prominence
Frist pledged to leave the Senate after two terms in 2006, and did not run in the 2006 Republican primary for his Senate seat. He campaigned heavily for Republican nominee Bob Corker, who won by a small margin over Congressman Harold Ford, Jr. in the general election.
Frist had been widely seen as a potential presidential candidate for the Republican party in 2008, much in the same tradition as Bob Dole, a previous holder of the Senate Majority Leader position. On November 28, 2006, however, he announced that he had decided not to run, and would return to the field of medicine.

Political Future
Frist has been married to his wife, Karyn, whom he met at a Boston emergency hospital, since 1982. They have three sons: Harrison, Jonathan, and Bryan. Harrison is a 2006 graduate of Princeton University, Jonathan is a student at Vanderbilt University, and Bryan is a first-year student at Princeton University. The Frist family are members of the National Presbyterian Church in Washington, D.C..
Frist has been a pilot since the age of 16. He holds commercial, instrument and multi-engine ratings. He has also run seven marathons and two half-marathons.
In June, 1989, Frist published his first book, Transplant: A Heart Surgeon's Account of the Life-And-Death Dramas of the New Medicine, in which he wrote, "A doctor is a man whose job justifies everything . . . Life [is] a gift, not an inalienable right."
With J.H. Helderman, he edited "Grand Rounds in Transplantation" in 1995. In October, 1999, Frist co-authored Tennessee Senators, 1911–2001: Portraits of Leadership in a Century of Change with J. Lee Annis, Jr. In March, 2002, Frist published his third book, When Every Moment Counts: What You Need to Know About Bioterrorism from the Senate's Only Doctor. While generally well received, the book later spurred accusations of hypocrisy regarding his remarks about Richard Clarke. When Clarke published his book Against All Enemies in 2004, Frist stated "I am troubled that someone would sell a book, trading on their service as a government insider with access to our nation's most valuable intelligence, in order to profit from the suffering that this nation endured on September 11, 2001." In December 2003, Frist and coauthor Shirley Wilso released Good People Beget Good People: A Genealogy of the Frist Family, Frist has also written numerous medical articles.
In 1998 he visited African hospitals and schools with the Christian aid group Samaritan's Purse. Frist has continued to make medical mission trips to Africa every year since 1998. In Africa, he has operated on people and saved lives. He has also been vocal in speaking out against the genocide occurring in Darfur.

Bill Frist Financial status


Main article: Terri SchiavoBill Frist Schiavo case

Main article: Bill Frist medical school experiments controversy Medical school experiments
Frist's primary legislative focus has been on issues of concern to the health care industry. The senator also opposes abortion. In the Senate, he led the fight against intact dilation and extraction. He voted for the Partial-Birth Abortion Act of 2003, voted against an amendment to include a woman's health exception (as he considered the procedure to be hazardous to a woman's health), and is opposed to all federal funding of abortion. Frist supports a total ban on human cloning, even for purposes of stem cell research. He supports programs to fight AIDS and African poverty. He travels to Africa frequently to provide medical care.
Since 2001, Frist had stood beside Bush in his insistence that only currently existing lines be used for stem cell research. But in July 2005, after severely criticizing the MLO, Frist reversed course and endorsed a House-passed plan to expand federal funding of the research, saying "it's not just a matter of faith, it's a matter of science." Up to that point the legislation had been considered bottled up in the Senate. The decision quickly drew criticism from some Christian fundamentalists such as Dr. James Dobson, but garnered praise from some Democrats and many Republicans, including former First Lady Nancy Reagan.
He opposes same-sex marriage and gay adoption, and supports the death penalty. [1]
On education, Frist supports the No Child Left Behind Act, which passed in 2001 with bipartisan support. In August 2005 he announced his support for teaching intelligent design in public school science classes.
In November 2005, Frist told reporters that he was less concerned about possible torture at CIA secret prisons than he was about potentially compromising the security of millions of Americans.
Flying home after visiting the Guantanamo Bay detention center he said September 10, 2006 he expects bipartisan support for putting top captured al-Qaida figures on trial before military commissions and for guidelines on how they should be treated. Frist visited the detention center in eastern Cuba, which holds some 460 detainees, including 14 top alleged al-Qaida figures recently transferred from CIA custody. Frist said his visit with fellow Republicans Sen. Mitch McConnell of Kentucky and Sen. Arlen Specter of Pennsylvania, the Senate Judiciary Committee chairman, was especially poignant coming one day short of the fifth anniversary of the attacks that killed nearly 3,000 people. Frist told that to be there with the recognition that 14 individuals were there who in all likelihood contributed to the September 11, 2001 attacks led him to think how critical it is that we do define the appropriate criteria to make sure we get information to prevent such a tragedy from ever occurring again. The senators didn't see the 14 new detainees and instead visited Guantanamo to learn of interrogation techniques he said. In his mind, the detainees are being treated in a safe and humane way.

Ideology and issues

Official — 'Bill Frist (R-TN) Top Industries' (top industries supporting Bill Frist), Center for Responsive Politics — 'Bill Frist (R-TN) Top Contributors' Top Contributors' (top contributors supporting Bill Frist), Center for Responsive Politics

Thursday, August 30, 2007

This article is part of the series: Politics and government of Belarus
The elections for the position of president of Belarus took place on March 19, 2006. The winner of the elections holds the office until the next round of scheduled elections, which are determined by the country's House of Representatives.
Western observers have deemed the elections rigged. The Organization for Security and Co-operation in Europe (OSCE) declared that the election "failed to meet OSCE commitments for democratic elections." However, election observers from the Commonwealth of Independent States (CIS) described the vote as open and transparent.


  • Alexander Lukashenko

    • Prime Minister: Sergey Sidorsky
      National Assembly

      • Council of the Republic
        House of Representatives
        People's Assembly
        Constitutional Court
        Supreme Court
        Economic Court
        Foreign relations
        Administrative division
        Political parties Candidates

        Zianon Pazniak: withdrew on January 26
        Valery Frolov: withdrew on February 1 in favor of Kazulin
        Aleksandr Voitovich: withdrew on January 9
        Sergei Skrebets: withdrew in late January, supports Kazulin Belarusian presidential election, 2006 Preceding events
        On March 19, 2006 exit polls showed Lukashenko winning a third term in a landslide, amid opposition claims of vote-rigging and fear of violence. The EcooM organization gave Lukashenko 84.2% of the vote and Milinkevich just 2 percent, while the Belarusian Committee of Youth Organizations gave Lukashenko 84.2% and Milinkevich 3.1 percent. The Gallup Organization has noted that EcooM and the Belarusian Committee of Youth Organizations are government-controlled and both released their exit poll results before noon on election day, although voting stations closed at 8 p.m. [1]
        Lukashenko was sworn in for his third term on April 8, 2006.


        Belarus authorities initially vowed to crush unrest in the event of large-scale protests following the election. One of the protesters was killed in the fight. Four explosions were heard, apparently percussion grenades set off by police. Many protesters were detained, including one of the opposition leaders, Alexander Kozulin, Russian news agencies reported. The main opposition leader, Alexander Milinkevich, denied reports by Russian news agencies that he himself was detained.
        On March 29, reported that opposition leader Kozulin is arrested and can get up to 6 years in jail for organizing riots and hooliganism. Milinkevich can get 15 days for hooliganism.[2]
        According to Moscow News[3], two journalists of Belarus state television channel allegedly beaten by opposition forces during an unsanctioned rally in Minsk are currently in hospital with severe injuries, RIA Novosti said Monday. Reporter of First Belarusian State Channel Mikhail Kristin has suffered a concussion, and cameraman Dmitry Chumak has a spine injury. Both are in hospital, the Belarusian State Television company said. The journalists were injured during the Saturday unrest in the Belarus capital. Members of opposition called it a lie.

        Belarusian authorities
        After the results were announced, a mass rally assembled in October Square in Minsk, waving the banned white-red-white flag of independent Belarus, the flag of the European Union, as well as flags of other countries such as neighboring Russia, Poland and Ukraine, and even Armenia.
        The crowd of demonstrators rallying after the election was the biggest the opposition had mustered in years, reaching at least 10,000 to 20,000.. Most of the arrested people were sentenced 5 to 15 days in prison. There were Russian, Polish, Ukrainian, Canadian, and Georgian citizens among the arrested.
        On Saturday several thousand demonstrators took to the streets, as the police had closed off October Square. Opposition leader Alaksandar Kazulin was arrested. One of the demonstators was killed when the riot police dispersed the crowd.

        Belarusian opposition
        The official OSCE report released on March 20, 2006, concluded that the presidential election failed to meet OSCE commitments for democratic elections. The OSCE, of which Belarus is a member, stated that Lukashenko permitted State authority to be used in a manner which did not allow citizens to freely and fairly express their will at the ballot box, and a pattern of intimidation and the suppression of independent voices was evident EU diplomats are drawing up a list of Belarusian officials who will be targeted by "smart sanctions" and final decisions will be taken on 10 April.

        According to a Belarusian news portal, Lukashenko himself stated that the "last Presidential elections were rigged; I already told this to the Westerners. [...] 93.5% voted for the President Lukashenko [sic]. They said it's not a European number. We made it 86. This really happened. And if [one is to] start recounting the votes, I don't know what to do with them. Before the elections they told us that if we showed the European numbers, our elections would be accepted. We were planning to make the European numbers. But, as you can see, this didn't help either."[5]

Tuesday, August 28, 2007

Double (baseball)
In baseball, a double is the act of a batter striking the pitched ball and safely reaching second base without being called out by the umpire, without the benefit of a fielder's misplay (see error) or another runner being put out on a fielder's choice.
Typically, a double is a well-hit ball into the outfield that either finds the "gap" between the center fielder and one of the corner outfielders, bounces off the outfield wall and down into the field of play, or is hit up one of the two foul lines. To hit many doubles, one must have decent hitting skill and power; it also helps to run well enough to beat an outfield throw.
Doubles typically drive in runs from third base, second base, and even from first base at times. When total bases and slugging percentages are calculated, the number two is used for the calculation. The all-time leader in doubles is Tris Speaker, with 792.
A two-base hit awarded by an umpire when a batted ball is hit fairly and bounces out of play is referred to as a ground rule double. The batter is awarded second base and any runners advance two bases from the base they occupied at the time of the pitch. Prior to 1931, such hits were considered home runs. A two-base hit awarded because the batter hit into a special situation defined in the ground rules is also defined as a ground rule double. An example of this occurs where the rules of Wrigley Field (Chicago, Illinois) award a ground rule double if a batted ball hangs in the vines on the outfield bleacher wall. The rules of the Hubert H. Humphrey Metrodome (Minneapolis, Minnesota) award a ground rule double if the ball becomes stuck in the Teflon ceiling. (This has happened only once; Dave Kingman hit a ball into the ceiling during a 1984 game.)

Double (baseball) Career

Earl Webb (1931) - 67
George Burns (1926) - 64
Joe Medwick (1936) - 64
Hank Greenberg (1934) - 63
Paul Waner (1932) - 62

Monday, August 27, 2007

National team caps and goals correctDavid James (footballer) as of 24 December, 2006.David James (footballer) * Appearances (Goals)
David Benjamin James (born 1 August 1970, Welwyn Garden City, England) is an English professional footballer who currently plays for Portsmouth in the Premier League. He has played as a goalkeeper for a number of English clubs, and most notably won the 1995 League Cup with Liverpool.

International career
The section could be improved by integrating relevant items into the main text and removing inappropriate items.

He writes a fortnightly column for The Observer
During the 2003 close season, James was a guest at the training camp of American football team, the Miami Dolphins, where he worked out with the team and studied their training and conditioning methods. This has led to speculation that he is keen on a coaching role after he retires
He holds the record for the most clean sheets in the Premiership, with 142.
His children went to Bromsgrove School.
He went to the Sir Frederic Osbourn School in Welwyn Garden City - the same school as Nick Faldo and Lisa Snowden
James lives a mile from the small town off Chudleigh in Devon. He is often spotted in the town centre.

Sunday, August 26, 2007

Mathematical BridgeMathematical Bridge
The Mathematical Bridge is the popular name of a wooden bridge across the River Cam, between two parts of Queens' College, Cambridge. Its official name is merely the Wooden Bridge.
The bridge was designed by William Etheridge, and built by James Essex in 1749. It has been rebuilt on two occasions — 1867 and 1902 — but has kept the same overall design.
The title of 'Mathematical Bridge' was also given to one of the former bridges of the Cam between Trinity and Trinity Hall, also designed by James Essex, where Garret Hostel Bridge now stands.

See also

List of bridges in Cambridge

Saturday, August 25, 2007

Richard Hamilton
Richard Hamilton may refer to:
Richard Hamilton (actor) (1920–2004)
Richard Hamilton (architect), American architect and cofounder of Goody, Clancy & Associates, Inc
Richard Hamilton (artist) (born 1922), British painter and collage artist
Richard Hamilton (basketball) (born 1978)
Richard Hamilton (professor) (born 1943), mathematics professor
Richard Hamilton (sailor) (1836–1881), American Civil War sailor
Richard Hamilton (boxer), Jamaican, competed at the 1988 Summer Olympics

Friday, August 24, 2007

DVD (also known as "Digital Versatile Disc" or "Digital Video Disc") is a popular optical disc storage media format used for data storage. Primarily uses are for movies, software, and data backup purposes, DVDs are of the same form factor as compact discs (CDs), but allow for 8 times the data storage capacity (single-layer, single-sided).
All read-only DVD discs, regardless of type, are DVD-ROM discs. This includes replicated (factory pressed), recorded (burned), video, audio, and data DVDs. A DVD with properly formatted and structured video content is a DVD-Video disc. DVDs with properly formatted and structured audio content are DVD-Audio discs. Everything else, (including other types of DVD discs with video content) is referred to as a DVD-Data disc. Consumers use the term "DVD-ROM" to refer to pressed data discs only, but that is incorrect usage; moreover, the term DVD is also applied generically in describing newer video disc formats, Blu-ray Disc and HD DVD.

Optical disc
Optical disc image
Recorder hardware
Authoring software
Recording technologies

  • Recording modes
    Packet writing
    Compact Disc/CD-ROM: CD-R, CD-RW
    Blu-ray Disc: BD-R, BD-RE
    Holographic data storage
    3D optical data storage
    History of optical storage media
    Rainbow Books
    File systems

    • ISO 9660

      • Joliet
        Rock Ridge

        • Amiga Rock Ridge extensions
          El Torito
          Apple ISO9660 Extensions
          Universal Disk Format

          • Mount Rainier DVD-ROM History
            "DVD" was originally used as an initialism for the unofficial term "digital videodisk". The official DVD specification documents have never defined DVD. Usage in the present day varies, with "DVD", "Digital Video Disc", and "Digital Versatile Disc" all being common.

            The 12cm type is a standard DVD, and the 8cm variety is known as a mini-DVD. These are the same sizes as a standard CD and a mini-CD, respectively.
            Note: GB here means gigabyte, equal to 10 (or 1,073,741,824) bytes.
            Example: A disc with 8.5 GB capacity is equivalent to: (8.5 × 1,000,000,000) / 1,073,741,824 ≈ 7.92 GiB.
            Size Note: There is a difference in size between + and - DL DVD formats. For example, the 12 cm single sided disk has capacities:

            Capacity nomenclature
            DVD uses 650 nm wavelength laser diode light as opposed to 780 nm for CD. This permits a smaller spot on the media surface that is 1.32 µm for DVD while it was 2.11 µm for CD.
            Writing speeds for DVD were 1x, that is 1350 kB/s (≈1.32 MiB/s), in first drives and media models. More recent models at 18x or 20x will have 18 or 20 times that speed. Note that for CD drives, 1x means 153.6 kB/s (150 KiB/s), 9 times slower. DVD FAQ

            DVD-ROM Technology
            Main article: DVD recordable
            HP initially developed recordable DVD media from the need to store data for back-up and transport.
            DVD recordables are now also used for consumer audio and video recording. Three formats were developed: -R/RW (minus/dash), +R/RW (plus), -RAM (which is strictly speaking not random access memory).

            DVD recordable and rewriteable
            Dual Layer recording allows DVD-R and DVD+R discs to store significantly more data, up to 8.5 Gigabytes per disc, compared with 4.7 Gigabytes for single-layer discs. DVD-R DL was developed for the DVD Forum by Pioneer Corporation, DVD+R DL was developed for the DVD+RW Alliance by Philips and Mitsubishi Kagaku Media (MKM). Many current DVD recorders support dual-layer technology, and the price is comparable to that of single-layer drives, though the blank media remain significantly more expensive.

            Dual layer recording

            Main article: DVD-Video DVD-Video

            Main article: DVD-Audio DVD-Audio

            Main article: CPRM Security
            There are several possible successors to DVD being developed by different consortiums: Sony/Panasonic's Blu-ray Disc (BD), Toshiba's HD DVD and Maxell's Holographic Versatile Disc (HVD).
            In April 2000, Sonic Solutions and Ravisent announced hDVD, an HDTV extension to DVD that presaged the HD formats that debuted 6 years later.
            On April 15, 2004, in a co-op project with TOPPAN Printing Co., the electronics giant Sony Corp. successfully developed the paper disc, a storage medium that is made out of 51% paper and offers up to 25 GB of storage, about five times more than the standard 4.7 GB DVD. The disc can be easily cut with scissors and recycled offering an environmentally friendly storage medium.
            As reported in a mid 2005 issue of Popular Mechanics, it is not yet clear which technology will win the format war over DVD. HD DVD discs have a lower capacity than Blu-ray Discs (15 GB vs. 25 GB for single layer, 30 GB vs. 50 GB for dual layer). Other speculations as to which format will win include Blu-ray Disc's larger hardware vendor and movie studio support, and HD DVD's faster read times.
            This situation—multiple new formats fighting as the successor to a format approaching purported obsolescence—previously appeared as the "war of the speeds" in the record industry of the 1950s. It is also similar to the VHS/Betamax war in consumer video recorders in the late 1980s.
            The new generations of optical formats have restricted access through many various digital rights management schemes such as AACS and HDCP; it remains to be seen what impact the limitation of fair use rights has on their adoption in the marketplace.

            Competitors and successors

            CD and DVD packaging
            DIVX disposable DVD
            DVD authoring
            DVD cover art
            DVD Formats
            DVD Talk
            DVD TV Games
            DVD-D disposable DVD
            Enhanced Versatile Disc
            Super Audio CD
            User operation prohibition
            Flexplay disposable DVD
            HD DVD
            Blu-ray Disc
            Inkjet printable DVD
            List of DVD manufacturers
            MultiLevel Recording
            Special edition
            DVD-RAM Official

            Dual Layer Explained – Informational Guide to the Dual Layer Recording Process

Thursday, August 23, 2007

Central Provident Fund
The Central Provident Fund (Abbreviation: CPF; Chinese: 公积金, Pinyin: Gōngjījīn) is a compulsory comprehensive social security savings plan which aims to provide working Singaporeans with a sense of security and confidence in their old age. It is administered by the Central Provident Fund Board, a statutory board under the Ministry Of Manpower. The CPF was started on 1 July 1955. The overall scope and benefits of the CPF encompass the following:
Working Singaporeans and their employers make monthly contributions to the CPF and these contributions go into three accounts:
As a social security plan, some of its policies have encountered criticisms, some of which are against social security in general and its compulsory nature, as well as the policy in management of the money.
Schooling children will later on have their outstanding funds in their Edusave account deposited into their CPF account.

Home Ownership
Family Protection
Asset Enhancement
Ordinary Account - the savings can be used to buy a home, pay for CPF insurance, investment and education.
Special Account - for old age, contingency purposes and investment in retirement-related financial products.
Medisave Account - the savings can be used for hospitalisation expenses and approved medical insurance.

Wednesday, August 22, 2007

Richard Lovell Edgeworth
Richard Lovell Edgeworth (May 31, 1744-June 13, 1817) was an English writer and inventor. Born in Bath, England, he lived in Ireland. He was the father of Maria Edgeworth and 21 other children, (by his 4 wives), and grandfather to Francis Ysidro Edgeworth. A Trinity College and Oxford alumnus, he is credited, among other inventions, for creating a machine to measure the size of a plot of land. He also made strides in the developing educational methods. He invented the caterpillar track in 1770. He died June 13, 1817.

Tuesday, August 21, 2007

Don Redman
Donald Matthew Redman (July 29, 1900, Piedmont, West Virginia - November 30, 1964, New York) was an American jazz musician, arranger, and composer.
Redman was born in Piedmont, West Virginia. His father was a music teacher, his mother was a singer. Don began playing the trumpet at the age of 3, joined his first band at 6 and by age 12 he was proficient on all wind instruments ranging from trumpet to oboe as well as piano. He studied at at Storer's College in Harper's Ferry and at the Boston Conservatory, then joined Billy Page's Broadway Syncopaters in New York City.
In 1923 Don Redman joined the Fletcher Henderson orchestra, mostly playing clarinet and saxophones. He soon began assisting in writing arrangements, and Redman did much to formulate the sound that was to become big band Swing. (It's significant to note that with a few exceptions, Henderson really didn't start arranging until the mid 1930's.) Redman did the bulk of arrangements (through 1927) and after he left Benny Carter took over arranging for the band.
His importance in the formulation of arranged hot jazz can not be overstated; a chief trademark of Redman's arrangements was that he harmonized melody lines and pseudo-solos within separate sections; for example, clarinet, sax, or brass trios. He played these sections off each other, having one section punctuate the figures of another, or moving the melody around different orchestral sections and soloists. His use of this technique was sophisticated, highly innovative, and formed the basis of much big band jazz writing in the following decades.
In 1927 Redman joined the Detroit, Michigan based band McKinney's Cotton Pickers as their Muscial Director and Leader. He was responsible for their great success and arranged about half of their music (spliting the arranging duties with John Nesbitt through 1931). Redman was occasionally featured as their vocalist, displaying his humorous vocal style.
Redman then formed his own band (featuring, for a time, Fletcher Henderson's younger brother Horace on piano), which got a residency at the famous Manhattan jazz club Connie's Inn. Redman's band got a recording contract with Brunswick Records and a series of radio broadcasts. Redman's band was even featured doing the soundtrack of a Betty Boop cartoon ("I Heard")[1] featuring Redman compositions. Notable musicians in Redman's band included Sidney De Paris, trumpet, Edward Inge, clarinet, and singer Harlan Lattimore, who was known as "The Colored Bing Crosby". On the side Redman also did arrangements for other band leaders and musicians, including Paul Whiteman, Isham Jones, and Bing Crosby.
In 1937, Redman pioneered a series of swing re-arrangements of old classic pop tunes for the Variety label. His use of a swinging vocal group (called "The Swing Choir") was very modern and even today, a bit usual.
In 1940 Redman disbanded his orchestra, and concentrated on freelance work writing arrangements; some of his arrangements became hits for Jimmy Dorsey, Count Basie, and Harry James.
Don Redman had a musical television show on the CBS network for the 1949 season. In the 1950s he was music director for singer Pearl Bailey.
In the early 1960s he played piano for the Georgia Minstrels Concert and soprano sax with Eubie Blake & Noble Sissle's band.
Don Redman died in New York City on November 30, 1964.

Sunday, August 19, 2007

Nordstrom Sisters
The Nordstrom Sisters were an American sister act from 19311976. Originally from Chicago, they were billed as society performers. These international cabaret singers were often styled as "The Misses Nordstrom" or introduced as "those Park Avenue darlings, the Nordstrom Sisters".
Their songs were full of sexual innuendo and double entendre. They were of Swedish and Norwegian extraction.

Saturday, August 18, 2007

KDevelop is a free IDE for GNU/Linux and other Unix-like operating systems. KDevelop is licensed under the GPL license.
KDevelop does not include a compiler; instead, it uses the GNU Compiler Collection (or, optionally, another compiler) to produce executable code.
The current version, 3.4, supports many programming languages such as Ada, Bash, C, C++, Fortran, Java, Pascal, Perl, PHP, Python, Ruby, and SQL.

KDevelop Features

Comparison of integrated development environments

Friday, August 17, 2007

In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will terminate in a defined end-state.
The concept of an algorithm originated as a means of recording procedures for solving mathematical problems such as finding the common divisor of two numbers or multiplying two numbers. A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") that David Hilbert posed in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (cf Kleene 1943:274) or "effective method" (cf Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation I" of 1936, and Alan Turing's Turing machines of 1936-7 and 1939.

No generally accepted formal definition of "algorithm" exists yet. We can, however, derive clues to the issues involved and an informal meaning of the word from the following quotation from Boolos and Jeffrey (1974, 1999):
"No human being can write fast enough, or long enough, or small enough to list all members of an enumerably infinite set by writing out their names, one after another, in some notation. But humans can do something equally useful, in the case of certain enumerably infinite sets: They can give explicit instructions for determining the nth member of the set, for arbitrary finite n. Such instructions are to be given quite explicitly, in a form in which they could be followed by a computing machine, or by a human who is capable of carrying out only very elementary operations on symbols" (boldface added, p. 19).
The words "enumerably infinite" mean "countable using integers perhaps extending to infinity". Thus Boolos and Jeffrey are saying that an algorithm implies instructions for a process that "creates" output integers from an arbitrary "input" integer or integers that, in theory, can be chosen from 0 to infinity. Thus we might expect an algorithm to be an algebraic equation such as y = m + n — two arbitrary "input variables" m and n that produce an output y. Unfortunately — as we see in Algorithm characterizations — the word algorithm implies much more than this, something on the order of (for our addition example):
Precise instructions (in language understood by "the computer") for a "fast, efficient, good" process that specifies the "moves" of "the computer" (machine or human, equipped with the necessary internally-contained information and capabilities) to find, decode, and then munch arbitrary input integers/symbols m and n, symbols + and = ... and (reliably, correctly, "effectively") produce, in a "reasonable" time, output-integer y at a specified place and in a specified format.
The concept of algorithm is also used to define the notion of decidability (logic). That notion is central for explaining how formal systems come into being starting from a small set of axioms and rules. In logic, the time that an algorithm requires to complete cannot be measured, as it is not apparently related with our customary physical dimension. From such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete (in some sense) and abstract usage of the term.
For a detailed presentation of the various points of view around the definition of "algorithm" see Algorithm characterizations. For examples of simple addition algorithms specified in the detailed manner described in Algorithm characterizations, see Algorithm examples.

Why algorithms are necessary: an informal definition
Algorithms are essential to the way computers process information, because a computer program is essentially an algorithm that tells the computer what specific steps to perform (in what specific order) in order to carry out a specified task, such as calculating employees' paychecks or printing students' report cards. Thus, an algorithm can be considered to be any sequence of operations that can be performed by a Turing-complete system. Authors who assert this thesis include Savage (1987) and Gurevich (2000):
"...Turing's informal argument in favor of his thesis justifies a stronger thesis: every algorithm can be simulated by a Turing machine" (Gurevich 2000:1) ...according to Savage [1987], "an algorithm is a computational process defined by a Turing machine."(Gurevich 2000:3)
Typically, when an algorithm is associated with processing information, data are read from an input source or device, written to an output sink or device, and/or stored for further processing. Stored data are regarded as part of the internal state of the entity performing the algorithm. In practice, the state is stored in a data structure, but an algorithm requires the internal data only for specific operation sets called abstract data types.
For any such computational process, the algorithm must be rigorously defined: specified in the way it applies in all possible circumstances that could arise. That is, any conditional steps must be systematically dealt with, case-by-case; the criteria for each case must be clear (and computable).
Because an algorithm is a precise list of precise steps, the order of computation will almost always be critical to the functioning of the algorithm. Instructions are usually assumed to be listed explicitly, and are described as starting 'from the top' and going 'down to the bottom', an idea that is described more formally by flow of control.
So far, this discussion of the formalization of an algorithm has assumed the premises of imperative programming. This is the most common conception, and it attempts to describe a task in discrete, 'mechanical' means. Unique to this conception of formalized algorithms is the assignment operation, setting the value of a variable. It derives from the intuition of 'memory' as a scratchpad. There is an example below of such an assignment.
For some alternate conceptions of what constitutes an algorithm see functional programming and logic programming .

Algorithms can be expressed in many kinds of notation, including natural languages, pseudocode, flowcharts, and programming languages. Natural language expressions of algorithms tend to be verbose and ambiguous, and are rarely used for complex or technical algorithms. Pseudocode and flowcharts are structured ways to express algorithms that avoid many of the ambiguities common in natural language statements, while remaining independent of a particular implementation language. Programming languages are primarily intended for expressing algorithms in a form that can be executed by a computer, but are often used as a way to define or document algorithms.
There is a wide variety of representations possible and one can express a given Turing machine program as a sequence of machine tables (see more at finite state machine and state transition table), as flowcharts (see more at state diagram), or as a form of rudimentary machine code or assembly code called "sets of quadruples" (see more at Turing machine).
Sometimes it is helpful in the description of an algorithm to supplement small "flow charts" (state diagrams) with natural-language and/or arithmetic expressions written inside "block diagrams" to summarize what the "flow charts" are accomplishing.
Representations of algorithms are generally classed into three accepted levels of Turing machine description (Sipser 2006:157):

"...prose to describe an algorithm, ignoring the implementation details. At this level we do not need to mention how the machine manages its tape or head"

"...prose used to define the way the Turing machine uses its head and the way that it stores data on its tape. At this level we do not give details of states or transition function"

Most detailed, "lowest level", gives the Turing machine's "state table".
For an example of the simple algorithm "Add m+n" described in all three levels see Algorithm examples.

1 High-level description:
2 Implementation description:
3 Formal description: Expressing algorithms
Most algorithms are intended to be implemented as computer programs. However, algorithms are also implemented by other means, such as in a biological neural network (for example, the human brain implementing arithmetic or an insect looking for food), in an electrical circuit, or in a mechanical device.

One of the simplest algorithms is to find the largest number in an (unsorted) list of numbers. The solution necessarily requires looking at every number in the list, but only once at each. From this follows a simple algorithm, which can be stated in a high-level description English prose, as:
High-level description:
(Quasi-) Formal description: Written in prose but much closer to the high-level language of a computer program, the following is the more formal coding of the algorithm in pseudocode or pidgin code:
For a more complex example of an algorithm, see Euclid's algorithm for the greatest common divisor, one of the earliest algorithms known.

Assume the first item is largest.
Look at each of the remaining items in the list and if it is larger than the largest item so far, make a note of it.
The last noted item is the largest in the list when the process is complete.
"←" is a loose shorthand for "changes to". For instance, "largestitem" means that the value of largest changes to the value of item.
"return" terminates the algorithm and outputs the value that follows. Example
As it happens, it is important to know how much of a particular resource (such as time or storage) is required for a given algorithm. Methods have been developed for the analysis of algorithms to obtain such quantitative answers; for example, the algorithm above has a time requirement of O(n), using the big O notation with n as the length of the list. At all times the algorithm only needs to remember two values: the largest number found so far, and its current position in the input list. Therefore it is said to have a space requirement of O(1). (Note that the size of the inputs is not counted as space used by the algorithm.)
Different algorithms may complete the same task with a different set of instructions in less or more time, space, or effort than others. For example, given two different recipes for making potato salad, one may have peel the potato before boil the potato while the other presents the steps in the reverse order, yet they both call for these steps to be repeated for all potatoes and end when the potato salad is ready to be eaten.
The analysis and study of algorithms is a discipline of computer science, and is often practiced abstractly without the use of a specific programming language or implementation. In this sense, algorithm analysis resembles other mathematical disciplines in that it focuses on the underlying properties of the algorithm and not on the specifics of any particular implementation. Usually pseudocode is used for analysis as it is the simplest and most general representation.

Algorithm analysis
There are various ways to classify algorithms, each with its own merits.

One way to classify algorithms is by implementation means.

Recursion or iteration: A recursive algorithm is one that invokes (makes reference to) itself repeatedly until a certain condition matches, which is a method common to functional programming. Iterative algorithms use repetitive constructs like loops and sometimes additional data structures like stacks to solve the given problems. Some problems are naturally suited for one implementation or the other. For example, towers of hanoi is well understood in recursive implementation. Every recursive version has an equivalent (but possibly more or less complex) iterative version, and vice versa.
Logical: An algorithm may be viewed as controlled logical deduction. This notion may be expressed as:
Algorithm = logic + control.

The logic component expresses the axioms that may be used in the computation and the control component determines the way in which deduction is applied to the axioms. This is the basis for the logic programming paradigm. In pure logic programming languages the control component is fixed and algorithms are specified by supplying only the logic component. The appeal of this approach is the elegant semantics: a change in the axioms has a well defined change in the algorithm.
Serial or parallel or distributed: Algorithms are usually discussed with the assumption that computers execute one instruction of an algorithm at a time. Those computers are sometimes called serial computers. An algorithm designed for such an environment is called a serial algorithm, as opposed to parallel algorithms or distributed algorithms. Parallel algorithms take advantage of computer architectures where several processors can work on a problem at the same time, whereas distributed algorithms utilise multiple machines connected with a network. Parallel or distributed algorithms divide the problem into more symmetrical or asymmetrical subproblems and collect the results back together. The resource consumption in such algorithms is not only processor cycles on each processor but also the communication overhead between the processors. Sorting algorithms can be parallelized efficiently, but their communication overhead is expensive. Iterative algorithms are generally parallelizable. Some problems have no parallel algorithms, and are called inherently serial problems.
Deterministic or non-deterministic: Deterministic algorithms solve the problem with exact decision at every step of the algorithm whereas non-deterministic algorithm solve problems via guessing although typical guesses are made more accurate through the use of heuristics.
Exact or approximate: While many algorithms reach an exact solution, approximation algorithms seek an approximation that is close to the true solution. Approximation may use either a deterministic or a random strategy. Such algorithms have practical value for many hard problems. Classification by implementation
Another way of classifying algorithms is by their design methodology or paradigm. There is a certain number of paradigms, each different from the other. Furthermore, each of these categories will include many different types of algorithms. Some commonly found paradigms include:

Divide and conquer. A divide and conquer algorithm repeatedly reduces an instance of a problem to one or more smaller instances of the same problem (usually recursively), until the instances are small enough to solve easily. One such example of divide and conquer is merge sorting. Sorting can be done on each segment of data after dividing data into segments and sorting of entire data can be obtained in conquer phase by merging them. A simpler variant of divide and conquer is called decrease and conquer algorithm, that solves an identical subproblem and uses the solution of this subproblem to solve the bigger problem. Divide and conquer divides the problem into multiple subproblems and so conquer stage will be more complex than decrease and conquer algorithms. An example of decrease and conquer algorithm is binary search algorithm.
Dynamic programming. When a problem shows optimal substructure, meaning the optimal solution to a problem can be constructed from optimal solutions to subproblems, and overlapping subproblems, meaning the same subproblems are used to solve many different problem instances, a quicker approach called dynamic programming avoids recomputing solutions that have already been computed. For example, the shortest path to a goal from a vertex in a weighted graph can be found by using the shortest path to the goal from all adjacent vertices. Dynamic programming and memoization go together. The main difference between dynamic programming and divide and conquer is, subproblems are more or less independent in divide and conquer, where as the overlap of subproblems occur in dynamic programming. The difference between the dynamic programming and straightforward recursion is in caching or memoization of recursive calls. When subproblems are independent and there is no repetition, memoization does not help;hence dynamic programming is not a solution for all complex problems. By using memoization or maintaining a table of subproblems already solved, dynamic programming reduces the exponential nature of many problems to polynomial complexity.
The greedy method. A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage; instead a "greedy" choice can be made of what looks best for the moment. The difference between dynamic programming and the greedy method is, it extends the solution with the best possible decision (not all feasible decisions) at an algorithmic stage based on the current local optimum and the best decision (not all possible decisions) made in previous stage. It is not exhaustive, and does not give accurate answer to many problems. But when it works, it will be the fastest method. The most popular greedy algorithm is finding the minimal spanning tree as given by Kruskal.
Linear programming. When solving a problem using linear programming, specific inequalities involving the inputs are found and then an attempt is made to maximize (or minimize) some linear function of the inputs. Many problems (such as the maximum flow for directed graphs) can be stated in a linear programming way, and then be solved by a 'generic' algorithm such as the simplex algorithm. A more complex variant of linear programming is called integer programming, where the solution space is restricted to the integers.
Reduction. This technique involves solving a difficult problem by transforming it into a better known problem for which we have (hopefully) asymptotically optimal algorithms. The goal is to find a reducing algorithm whose complexity is not dominated by the resulting reduced algorithm's. For example, one selection algorithm for finding the median in an unsorted list involves first sorting the list (the expensive portion) and then pulling out the middle element in the sorted list (the cheap portion). This technique is also known as transform and conquer.
Search and enumeration. Many problems (such as playing chess) can be modeled as problems on graphs. A graph exploration algorithm specifies rules for moving around a graph and is useful for such problems. This category also includes search algorithms, branch and bound enumeration and backtracking.
The probabilistic and heuristic paradigm. Algorithms belonging to this class fit the definition of an algorithm more loosely.
Probabilistic algorithms are those that make some choices randomly (or pseudo-randomly); for some problems, it can in fact be proven that the fastest solutions must involve some randomness.
Genetic algorithms attempt to find solutions to problems by mimicking biological evolutionary processes, with a cycle of random mutations yielding successive generations of "solutions". Thus, they emulate reproduction and "survival of the fittest". In genetic programming, this approach is extended to algorithms, by regarding the algorithm itself as a "solution" to a problem.
Heuristic algorithms, whose general purpose is not to find an optimal solution, but an approximate solution where the time or resources are limited. They are not practical to find perfect solutions. An example of this would be local search, taboo search, or simulated annealing algorithms, a class of heuristic probabilistic algorithms that vary the solution of a problem by a random amount. The name "simulated annealing" alludes to the metallurgic term meaning the heating and cooling of metal to achieve freedom from defects. The purpose of the random variance is to find close to globally optimal solutions rather than simply locally optimal ones, the idea being that the random element will be decreased as the algorithm settles down to a solution. Classification by design paradigm
See also: List of algorithms
Every field of science has its own problems and needs efficient algorithms. Related problems in one field are often studied together. Some example classes are search algorithms, sorting algorithms, merge algorithms, numerical algorithms, graph algorithms, string algorithms, computational geometric algorithms, combinatorial algorithms, machine learning, cryptography, data compression algorithms and parsing techniques.
Fields tend to overlap with each other, and algorithm advances in one field may improve those of other, sometimes completely unrelated, fields. For example, dynamic programming was originally invented for optimization of resource consumption in industry, but is now used in solving a broad range of problems in many fields.

Classification by field of study
See also: Complexity class
Algorithms can be classified by the amount of time they need to complete compared to their input size. There is a wide variety: some algorithms complete in linear time relative to input size, some do so in an exponential amount of time or even worse, and some never halt. Additionally, some problems may have multiple algorithms of differing complexity, while other problems might have no algorithms or no known efficient algorithms. There are also mappings from some problems to other problems. Owing to this, it was found to be more suitable to classify the problems themselves instead of the algorithms into equivalence classes based on the complexity of the best possible algorithms for them.

Classification by complexity
See also: Software patents for a general overview of the patentability of software, including computer-implemented algorithms.
Algorithms, by themselves, are not usually patentable. In the United States, a claim consisting solely of simple manipulations of abstract concepts, numbers, or signals do not constitute "processes" (USPTO 2006) and hence algorithms are not patentable (as in Gottschalk v. Benson). However, practical applications of algorithms are sometimes patentable. For example, in Diamond v. Diehr, the application of a simple feedback algorithm to aid in the curing of synthetic rubber was deemed patentable. The patenting of software is highly controversial, and there are highly criticized patents involving algorithms, especially data compression algorithms, such as Unisys' LZW patent.
Additionally, some cryptographic algorithms have export restrictions (see export of cryptography).

Legal issues

History: Development of the notion of "algorithm"
See also: Timeline of algorithms
The word algorithm comes from the name of the 9th century Persian mathematician Abu Abdullah Muhammad ibn Musa al-Khwarizmi whose works introduced Indian numerals and algebraic concepts. He worked in Baghdad at the time when it was the centre of scientific studies and trade. The word algorism originally referred only to the rules of performing arithmetic using Arabic numerals but evolved via European Latin translation of al-Khwarizmi's name into algorithm by the 18th century. The word evolved to include all definite procedures for solving problems or performing tasks.

Origin of the word
Tally-marks: To keep track of their flocks, their sacks of grain and their money the ancients used tallying — accumulating stones, or marks — discrete symbols in clay or scratched on sticks. Through the Babylonians and Egyptian use of marks and symbols eventually Roman numerals and the abacus evolved. (Dilson, p.16–41) Tally marks appear prominently in unary numeral system arithmetic used in Turing machine and Post-Turing machine computations.

Discrete and distinguishable symbols
The work of the ancient Greek geometers, Persian mathematician Al-Khwarizmi — often considered as the "father of algebra", and Western European mathematicians culminated in Leibniz's notion of the calculus ratiocinator (ca 1680):
"A good century and a half ahead of his time, Leibniz proposed an algebra of logic, an algebra that would specify the rules for manipulating logical concepts in the manner that ordinary algebra specifies the rules for manipulating numbers" (Davis 2000:18).

Manipulation of symbols as "place holders" for numbers: algebra
The clock: Bolter credits the invention of the weight-driven clock as "The key invention [of Europe in the Middle Ages]", in particular the verge escapement (Bolter 1984:24) that provides us with the tick and tock of a mechanical clock. "The accurate automatic machine" (Bolter 1984:26) led immediately to "mechanical automata" beginning in the thirteenth century and finally to "computational machines" – the difference engine and analytical engines of Charles Babbage and Countess Ada Lovelace (Bolter p.33–34, p.204–206).
Jacquard loom, Hollerith punch cards, telegraphy and telephony — the electromechanical relay: Bell and Newell (1971) indicate that the Jacquard loom (1801), precursor to Hollerith cards (punch cards, 1887), and "telephone switching technologies" were the roots of a tree leading to the development of the first computers (Bell and Newell diagram p. 39, cf Davis (2000)). By the mid-1800s the telegraph, as the precursor of the telephone, was in use throughout the world, its discrete and distinguishable encoding of letters as "dots and dashes" a common sound. By the late 1800s the ticker tape (ca 1870s) was in use, as were the use of Hollerith cards in the 1890 U.S. census, the Teletype (ca 1910) with its the use of punched-paper binary encoding Baudot code on tape.
Telephone-switching networks of electromechanical relays (invented 1835) was behind the work of George Stibitz (1937), the inventor of the digital adding device. As he worked in Bell Laboratories, he observed the "burdensome' use of mechanical calculators with gears. "He went home one evening in 1937 intending to test his idea.... When the tinkering was over, Stibitz had constructed a binary adding device" (Valley News, p. 13).
Davis (2000) observes the particular importance of the electromechanical relay (with its two "binary states" open and closed):
It was only with the development, beginning in the 1930s, of electromechanical calculators using electrical relays, that machines were built having the scope Babbage had envisioned." (Davis, p. 148)

Mechanical contrivances with discrete states
Symbols and rules: In rapid succession the mathematics of George Boole (1847, 1854), Gottlob Frege (1879), and Giuseppe Peano (1888–1889) reduced arithmetic to a sequence of symbols manipulated by rules. Peano's The principles of arithmetic, presented by a new method (1888) was "the first attempt at an axiomatization of mathematics in a symbolic language" (van Heijenoort:81ff).
But Heijenoort gives Frege (1879) this kudos: Frege's is "perhaps the most important single work ever written in logic. ... in which we see a " 'formula language', that is a lingua characterica, a language written with special symbols, "for pure thought", that is, free from rhetorical embellishments ... constructed from specific symbols that are manipulated according to definite rules"(van Heijenoort:1). The work of Frege was further simplified and amplified by Alfred North Whitehead and Bertrand Russell in their Principia Mathematica (1910–1913).
The paradoxes: At the same time a number of disturbing paradoxes appeared in the literature, in particular the Burali-Forti paradox (1897), the Russell paradox (1902–03), and the Richard Paradox (1905, Dixon 1906), (cf Kleene 1952:36–40). The resultant considerations led to Kurt Gödel's paper (1931) — he specifically cites the paradox of the liar — that completely reduces rules of recursion to numbers.
Effective calculability: In an effort to solve the Entscheidungsproblem defined precisely by Hilbert in 1928, mathematicians first set about to define what was meant by an "effective method" or "effective calculation" or "effective calculability" (i.e. a calculation that would succeed). In rapid succession the following appeared: Alonzo Church, Stephen Kleene and J.B. Rosser's λ-calculus (cf footnote in Alonzo Church 1936a:90, 1936b:110), a finely-honed definition of "general recursion" from the work of Gödel acting on suggestions of Jacques Herbrand (cf Gödel's Princeton lectures of 1934) and subsequent simplifications by Kleene (1935-6:237ff, 1943:255ff), Church's proof (Church 1936:88ff) that the Entscheidungsproblem was unsolvable, Emil Post's definition of effective calculability as a worker mindlessly following a list of instructions to move left or right through a sequence of rooms and while there either mark or erase a paper or observe the paper and make a yes-no decision about the next instruction (cf his "Formulation I" 1936:289-290), Alan Turing's proof of that the Entscheidungsproblem was unsolvable by use of his "a- [automatic-] machine" (Turing 1936-7:116ff) -- in effect almost identical to Post's "formulation", J. Barkley Rosser's definition of "effective method" in terms of "a machine" (Rosser 1939:226), S. C. Kleene's proposal of a precursor to the "Church thesis" that he called "Thesis I" (Kleene 1943:273–274)), and a few years later Kleene's renaming his "Thesis" "Church's Thesis" (Kleene 1952:300, 317) and proposing "Turing's Thesis (Kleene 1952:376).

Mathematics during the 1800s up to the mid-1900s
Here is a remarkable coincidence of two men not knowing each other but describing a process of men-as-computers working on computations — and they yield virtually identical definitions.
Emil Post (1936) described the actions of a "computer" (human being) as follows:
"...two concepts are involved: that of a symbol space in which the work leading from problem to answer is to be carried out, and a fixed unalterable set of directions.
His symbol space would be
"a two way infinite sequence of spaces or boxes... The problem solver or worker is to move and work in this symbol space, being capable of being in, and operating in but one box at a time.... a box is to admit of but two possible conditions, i.e. being empty or unmarked, and having a single mark in it, say a vertical stroke.
"One box is to be singled out and called the starting point. ...a specific problem is to be given in symbolic form by a finite number of boxes [i.e. INPUT] being marked with a stroke. Likewise the answer [i.e. OUTPUT] is to be given in symbolic form by such a configuration of marked boxes....
"A set of directions applicable to a general problem sets up a deterministic process when applied to each specific problem. This process will terminate only when it comes to the direction of type (C ) [i.e. STOP]." (U p. 289–290) See more at Post-Turing machine
Alan Turing's work (1936–1937, 1939:160) preceded that of Stibitz (1937); it is unknown if Stibitz knew of the work of Turing. Turing's biographer believed that Turing's use of a typewriter-like model derived from a youthful interest: "Alan had dreamt of inventing typewriters as a boy; Mrs. Turing had a typewriter; and he could well have begun by asking himself what was meant by calling a typewriter 'mechanical'" (Hodges, p. 96) Given the prevalence of Morse code and telegraphy, ticker tape machines, and Teletypes we might conjecture that all were influences.
Turing — his model of computation is now called a Turing machine — begins, as did Post, with an analysis of a human computer that he whittles down to a simple set of basic motions and "states of mind". But he continues a step further and creates a machine as a model of computation of numbers (Turing 1936-7:116):
"Computing is normally done by writing certain symbols on paper. We may suppose this paper is divided into squares like a child's arithmetic book....I assume then that the computation is carried out on one-dimensional paper, i.e. on a tape divided into squares. I shall also suppose that the number of symbols which may be printed is finite....
"The behavior of the computer at any moment is determined by the symbols which he is observing, and his "state of mind" at that moment. We may suppose that there is a bound B to the number of symbols or squares which the computer can observe at one moment. If he wishes to observe more, he must use successive observations. We will also suppose that the number of states of mind which need be taken into account is finite...
"Let us imagine that the operations performed by the computer to be split up into 'simple operations' which are so elementary that it is not easy to imagine them further divided" (Turing 1936-7:136).
Turing's reduction yields the following:
"The simple operations must therefore include:

"(a) Changes of the symbol on one of the observed squares
"(b) Changes of one of the squares observed to another square within L squares of one of the previously observed squares.
"It may be that some of these change necessarily invoke a change of state of mind. The most general single operation must therefore be taken to be one of the following:

"(A) A possible change (a) of symbol together with a possible change of state of mind.
"(B) A possible change (b) of observed squares, together with a possible change of state of mind"
"We may now construct a machine to do the work of this computer."((Turing 1936-7:136).
A few years later, Turing expanded his analysis (thesis, definition) with this forceful expression of it:
"A function is said to be "effectivey calculable" if its values can be found by some purely mechanical process. Although it is fairly easy to get an intuitive grasp of this idea, it is neverthessless desirable to have some more definite, mathematical expressible definition . . . [he discusses the history of the definition pretty much as presented above with respect to Gödel, Herbrand, Kleene, Church, Turing and Post] . . . We may take this statement literally, understanding by a purely mechanical process one which could be carried out by a machine. It is possible to give a mathematical description, in a certain normal form, of the structures of these machines. The development of these ideas leads to the author's definition of a computable function, and to an identification of computability † with effective calculability . . . .

"† We shall use the expression "computable function" to mean a function calculable by a machine, and we let "effectively calculabile" refer to the intuitive idea without particular identification with any one of these definitions." (Turing 1939:160).

Algorithms Emil Post (1936) and Alan Turing (1936-7, 1939)
J. Barkley Rosser boldly defined an 'effective [mathematical] method' in the following manner (boldface added):
"'Effective method' is used here in the rather special sense of a method each step of which is precisely determined and which is certain to produce the answer in a finite number of steps. With this special meaning, three different precise definitions have been given to date. [his footnote #5; see discussion immediately below]. The simplest of these to state (due to Post and Turing) says essentially that an effective method of solving certain sets of problems exists if one can build a machine which will then solve any problem of the set with no human intervention beyond inserting the question and (later) reading the answer. All three definitions are equivalent, so it doesn't matter which one is used. Moreover, the fact that all three are equivalent is a very strong argument for the correctness of any one." (Rosser 1939:225–6)
Rosser's footnote #5 references the work of (1) Church and Kleene and their definition of λ-definability, in particular Church's use of it in his An Unsolvable Problem of Elementary Number Theory (1936); (2) Herbrand and Gödel and their use of recursion in particular Gödel's use in his famous paper On Formally Undecidable Propositions of Principia Mathematica and Related Systems I (1931); and (3) Post (1936) and Turing (1936-7) in their mechanism-models of computation.
Stephen C. Kleene defined as his now-famous "Thesis I" known as "the Church-Turing Thesis". But he did this in the following context (boldface in original):
"12. Algorithmic theories... In setting up a complete algorithmic theory, what we do is to describe a procedure, performable for each set of values of the independent variables, which procedure necessarily terminates and in such manner that from the outcome we can read a definite answer, "yes" or "no," to the question, "is the predicate value true?"" (Kleene 1943:273)

J. B. Rosser (1939) and S. C. Kleene (1943)
A number of efforts have been directed toward further refinement of the definition of "algorithm", and activity is on-going because of issues surrounding, in particular, foundations of mathematics (especially the Church-Turing Thesis) and philosophy of mind (especially arguments around artificial intelligence). For more, see Algorithm characterizations.

See also