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Rhodesian Front

The Rhodesian Front was a conservative political party in Rhodesia when the country was under white minority rule. Led first by Winston Field, from 1964, by Ian Smith, the Rhodesian Front was the successor to the Dominion Party, the main opposition party in Southern Rhodesia during the Federation period; the RF was formed in March 1962 by Whites opposed to any immediate or short-term transition to Black majority rule. It won power at the general election that December. In successive elections between 1964 and 1979, the RF was returned with a large majority; the RF had fifteen founding principles, which included the preservation of each racial group's right to maintain its own identity, the preservation of'proper standards' through a policy of advancement through merit, the maintenance of the Land Apportionment Act, which formalised the racial imbalance in the ownership and distribution of land, opposition to compulsory racial integration, job protection for white workers, maintenance of the government's right to provide separate amenities for different races.

Following the elections leading to the country's independence in 1980, as the Republic of Zimbabwe, the RF won all 20 parliamentary seats reserved for whites in the power-sharing agreement that it had forged. On June 6, 1981, the party changed its name to the Republican Front, on July 21, 1984 it became the Conservative Alliance of Zimbabwe. Eleven of its twenty parliamentarians defected over the following four years, but the party again won 15 of the 20 parliamentary seats reserved for whites in the 1985 election. In 1986, the CAZ opened its membership to Zimbabweans of all races. In 1987 the ruling government abolished all reserved seats for whites; when these were abolished many white MPs joined the ruling ZANU party. Security Force Auxiliaries Rhodesians Never Die, Godwin, P. & Hancock, I. 1995. Baobab Books, Zimbabwe. Pollard, William C. A Career of Defiance: The Life of Ian Smith, Agusan River Publishing Co. 1992. Topeka, KS. McLaughlin, John. "Ian Smith and the Future of Zimbabwe," The National Review, October 30, 1981, pp. 2168–70.

Facts on File, 1984 ed. p. 574

Wayne Petti

Wayne Petti is a Canadian singer-songwriter best known the vocalist for indie rock band Cuff the Duke. He has contributed to projects by Blue Rodeo, The Hylozoists and Hayden in addition to releasing material on his own and as part of his side-project Grey Lands. Petti grew up in Ontario where he played with fellow Cuff the Duker Paul Lowman. After living for several years in Toronto, Petti relocated to Hamilton in 2014 where he lives with his wife and their son. Petti released City Lights Align, in 2007 on Outside Music, he has played with The Hylozoists. Life Stories for Minimum Wage - Cuff the Duke Cuff the Duke - Cuff the Duke La Fin Du Monde - The Hylozoists Sidelines of the City - Cuff the Duke City Lights Align - Wayne Petti Way Down Here - Cuff the Duke The Things We Left Behind - Blue Rodeo Morning Comes - - Cuff the Duke In Our Time - EP - - Cuff the Duke Union - 2012 - Cuff the Duke In Our Nature - - Blue Rodeo Songs by Other People - Grey Lands Right Arm - Grey Lands Cuff the Duke official website

Range segmentation

Range segmentation is the task of segmenting a range image, an image containing depth information for each pixel, into segments, so that all the points of the same surface belong to the same region, there is no overlap between different regions and the union of these regions generates the entire image. There have been two main approaches to the range segmentation problem: region-based range segmentation and edge-based range segmentation. Region-based range segmentation algorithms can be further categorized into two major groups: parametric model-based range segmentation algorithms and region-growing algorithms. Algorithms of the first group are based on assuming a parametric surface model and grouping data points so that all of them can be considered as points of a surface from the assumed parametric model. Region-growing algorithms start by segmenting an image into initial regions; these regions are merged or extended by employing a region growing strategy. The initial regions can be obtained using different methods, including random methods.

A drawback of algorithms of this group is that in general they produce distorted boundaries because the segmentation is carried out at region level instead of pixel level. Edge-based range segmentation algorithms are based on edge detection and labeling edges using the jump boundaries, they apply an edge detector to extract edges from a range image. Once boundaries are extracted, edges with common properties are clustered together. A typical example of edge-based range segmentation algorithms is presented by al.. The segmentation procedure starts by detecting discontinuities using zero-crossing and curvature values; the image is segmented at discontinuities to obtain an initial segmentation. At the next step, the initial segmentation is refined by fitting quadratics whose coefficients are calculated based on the Least squares method. In general, a drawback of edge-based range segmentation algorithms is that although they produce clean and well defined boundaries between different regions, they tend to produce gaps between boundaries.

In addition, for curved surfaces, discontinuities are smooth and hard to locate and therefore these algorithms tend to under-segment the range image. Although the range image segmentation problem has been studied for a number of years, the task of segmenting range images of curved surfaces is yet to be satisfactorily resolved. Image segmentation Image-based meshing Quantization IEEE International Conference on Computer Vision and Pattern Recognition 6th International Conference on Computer Vision, Bombay, 1998

Information extraction

Information extraction is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing. Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE focus on narrowly restricted domains. An example is the extraction from newswire reports of corporate mergers, such as denoted by the formal relation: M e r g e r B e t w e e n,from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp."A broad goal of IE is to allow computation to be done on the unstructured data. A more specific goal is to allow logical reasoning to draw inferences based on the logical content of the input data. Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context.

Information Extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission and display. The discipline of information retrieval has developed automatic methods of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis, IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events in a manner, similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts.

We define for any given IE task a template, a case frame to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator and weapon of the terroristic act, the date on which the event happened. An IE system for this problem is required to “understand” an attack article only enough to find data corresponding to the slots in this template. Information extraction dates back to the late 1970s in the early days of NLP. An early commercial system from the mid-1980s was JASPER built for Reuters by the Carnegie Group Inc</ref> with the aim of providing real-time financial news to financial traders. Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. MUC is a competition-based conference that focused on the following domains: MUC-1, MUC-2: Naval operations messages. MUC-3, MUC-4: Terrorism in Latin American countries. MUC-5: Joint ventures and microelectronics domain. MUC-6: News articles on management changes.

MUC-7: Satellite launch reports. Considerable support came from the U. S. Defense Advanced Research Projects Agency, who wished to automate mundane tasks performed by government analysts, such as scanning newspapers for possible links to terrorism; the present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the world wide web, refers to the existing Internet as the web of documents and advocates that more of the content be made available as a web of data; until this transpires, the web consists of unstructured documents lacking semantic metadata. Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with. A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted.

Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more machine-readable text to process the sentences. Typical IE tasks and subtasks include: Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, time, etc. from a newspaper article about a terrorist attack. Event extraction: Given an input document, output zero or more event templates. For instance, a newspaper article might describe multiple terrorist attacks. Knowledge Base Population: Fill a database of facts given a set of documents; the database is in the form of triplets, e.g. Named entity recognition: recognition of known entity names, place names, temporal expressions, certain types of numerical expressions, by employing existing knowledge of the domain or information extracted from other sentences

Mother Teresa: In the Name of God's Poor

Mother Teresa: In the Name of God's Poor is a 1997 made-for-television biographical film directed by Kevin Connor and starring Geraldine Chaplin as Mother Teresa. Mother Teresa herself withdrew her imprimatur shortly before her death, it was broadcast on what was known as The Family Channel on 5 October 1997. In mid-1940s Calcutta, Mother Teresa teaches geography at her convent. One day and one of the other sisters go outside the convent to find food for their girls, only to get caught up in a riot. Though they manage to make it back to the convent, Mother Teresa is shocked by the sight of the massive number of people starving in the streets. Haunted by the images of the hungry people, Mother Teresa decides to leave the convent to devote her life to caring for the poorest of the poor. Soon after her arrival in the slums, Mother Teresa teaches the children to read and write, but she faces opposition from the adults in the slum who mistrust her because of the colour of her skin; as Mother Teresa continues her crusade to help the poor, some of her former students from the convent come to her with the desire to become nuns and help her on her mission.

The films end scene sees Mother Teresa travelling to Norway to receive the Nobel Peace Prize. Geraldine Chaplin as Mother Teresa Keene Curtis as Father Van Exem Helena Carroll as Mother Superior David Byrd as Archbishop William Katt as Harry Harper Ravindra Randeniya as Police chief Belle Connor as Lorete Pupil Neil Daluwatte as Deputy Commissioner Chamitha de Alwis as Charu Upali De Silva as Angry Worshipper Nimmi Harasgama as Christina Sunil Hettiarachchi as Bald Man Veena Jayakody as Charu Ma W. Jayasiri as Mr. Goma Leonie Kotalawela as Hospital Nurse Hemasiri Liyanage as Manik Saumya Liyanage as Student Leader Anna Mathias Cornelia Hayes O'Herlihy as Sister Gabriella Yolanda Peiris as Mabel Goma Hilarian Perera as Dying Brahmin Prageeth Sanjeeva as Jyoti Roger Seneviratne as Student Leader Alan Shearman Peter Shepherd as Nobel Official Sangeetha Weeraratne as Sunitha Nilanthi Wijesinghe as Mrs. Goma Yashoda Wimaladharma as Sister Maria Ronnie Leitch as Hari The film was well received by critics.

At the time of the broadcast, The Philadelphia Inquirer applauded the film as a triumph for the network, as "probably the most important show it has presented in its 20-year history." Praise was lavished on the "authoritative" Chaplin. And without histrionics, she convincingly demonstrated Mother Teresa's absolute faith that God guides and God provides, despite opposition from both church and state." The review continued to praise how the film is "skilfully and winningly dramatized..and directed with assurance and passion". William Brailsford of The Washington Times noted that "Miss Chaplin gives a convincing performance as Mother Teresa, imitating her soft voice and her awkward yet charming mannerisms and re-creating that aura of piety that surrounded the "saint of the gutters." This remarkable actress has us in the palm of her hands early on, she never lets go." Brailsford praised the realism of the project, "The film's portrayal of the horrors of poverty and disease in India's streets is chillingly realistic.

With extreme vividness, some scenes will cause viewers to wince as they become bystanders to the insufferable agonies of the poor and starving."Caryn James of The New York Times praised Chaplin "Ms. Chaplin is fine in the role, full of quiet determination and faith". However, James felt, she seems like one more good woman. Whatever Mother Teresa might have thought of that, it doesn't go far as biography or drama."The film won the Audience Award at the 1998 Art Film Festival and the writers were nominated for the Humanitas Prize. Chaplin drew on her experience as a convent-educated schoolgirl in Switzerland and her once-held desire to become a nun. Names of God in Christianity Notable film portrayals of Nobel laureates Mother Teresa: In the Name of God's Poor on IMDb

Vernon Hargreaves

Vernon Hargreaves III is an American football cornerback, a free agent. He played college football at Florida and was drafted 11th overall by the Tampa Bay Buccaneers in the 2016 NFL Draft, he played for the Houston Texans. Hargreaves attended Wharton High School in Tampa, where he played football; as a sophomore in 2010, he had 44 tackles, two sacks, two interceptions. As a junior in 2011, he had two interceptions, along with 11 offensive touchdowns; as a senior in 2012, he had five interceptions. He was recognized. In addition to football, Hargreaves competed in field at Wharton; as a senior in 2012, he posted a personal-best time of 22.56 seconds in the 200-meter dash at the Hillsborough County National Division. At the FHSAA District Meet, he placed fourth in the 100-meter dash, second in the long jump and twelfth in the high jump. Regarded as a five-star recruit by, Hargreaves was ranked as the nation's best cornerback recruit and the second best player overall. Hargreaves accepted an athletic scholarship to attend the University of Florida, where he has played for head coaches Will Muschamp and Jim McElwain's Florida Gators football teams in Southeastern Conference competition in 2013, 2014, 2015.

As a true freshman in 2013, he started 10 of 12 games, recording 38 tackles and three interceptions, received first-team All-SEC honors at the cornerback position. As a sophomore, he played 12 games with three interceptions, 13 passes defended, two fumble recoveries on 50 tackles; as a junior, he made four interceptions, a forced fumble, four passes defended on 33 tackles in 12 games. After his junior year, he announced his intentions forgo his senior season and enter the 2016 NFL Draft. Hargreaves was projected to be a first round pick by NFL draft scouts, he was ranked the top cornerback prospect in the draft by and NFL analyst Daniel Jeremiah, was ranked the second best cornerback in the draft by NFL analysts Mike Mayock, Lance Zierlein, Bucky Brooks, was ranked the second best defensive back in the draft by Sports Illustrated and NFL analyst Charles Davis. The Tampa Bay Buccaneers selected Hargreaves in the first round of the 2016 NFL Draft, he was the third cornerback to be selected and the first of seven Florida Gators in 2016.

On May 6, 2016, the Tampa Bay Buccaneers signed Hargreaves to guaranteed four-year, $14.17 million contract that includes a signing bonus of $8.51 million. Throughout training camp, he participated in an open competition to name a starting cornerback against Brent Grimes, Johnthan Banks, Alterraun Verner, Jude Adjei-Barimah, Joel Ross. Head coach Dirk Koetter named Hargreaves the third cornerback on the depth chart, behind Brent Grimes and Alterraun Verner, he made his professional regular season debut and first career start in the Tampa Bay Buccaneers' season-opener at the Atlanta Falcons and recorded two combined tackles during a 31–24 victory. He made. On October 30, 2016, Hargreaves collected a season-high eight solo tackles and two pass deflections during a 30–24 overtime loss to the Oakland Raiders in Week 8. In Week 14, Hargreaves made three solo tackles, broke up a pass, made his first career interception off a pass by Drew Brees in the Buccaneers' 16–11 victory against the New Orleans Saints.

He finished his rookie season with 76 combined tackles, nine pass deflections, an interception in 16 games and 16 starts. Head coach Dirk Koetter retained Grimes and Hargreaves as the starting cornerbacks to begin the 2017 regular season. On October 1, 2017, Hargreaves recorded a career-high nine solo tackles and deflected a pass during a 25–23 victory against the New York Giants in Week 4. In Week 8, he tied his season-high of two pass deflections and recorded six combined tackles during a 17–3 loss to the Carolina Panthers. On November 12, 2017, Hargreaves made three combined tackles before exiting the Buccaneers' 15–10 win against the New York Jets in the third quarter after sustaining a hamstring injury, he was inactive for the next five games before the Buccaneers opted to place him on injured reserve on December 20, 2017. He finished his second season with 42 combined tackles and five pass deflections in nine games and eight starts. In Week 1, Hargreaves recorded seven combined tackles, one pass defensed and forced a fumble, recovered by Justin Evans and returned for a touchdown.

He suffered a shoulder injury in the game and was placed on injured reserve on September 12, 2018. On April 24, 2019, the Buccaneers exercised the fifth-year option on Hargreaves contract. In Week 1 against the San Francisco 49ers, Hargreaves intercepted Jimmy Garoppolo and returned it 15 yards for a touchdown in the 31–17 loss. In Week 2 against the Carolina Panthers, Hargreaves made 12 tackles, including making a pivotal stop during fourth down by pushing running back Christian McCaffrey out of bounds short of the first down marker in a 20–14 win. After starting the first nine games of the season, Hargreaves was waived by the Buccaneers on November 12, 2019. On November 13, 2019, Hargreaves was claimed off waivers by the Houston Texans, he was released on February 14, 2020. Tampa Bay Buccaneers bio Florida Gators bio