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Grace Wahba
Grace Goldsmith Wahba (born August 3, 1934) is an American statistician and now-retired I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison. She is a pioneer in methods for smoothing noisy data. Best known for the development of generalized cross-validation and " Wahba's problem", she has developed methods with applications in demographic studies, machine learning, DNA microarrays, risk modeling, medical imaging, and climate prediction. Biography Grace Wahba had an interest in science from an early age, when she was in Junior High she was given a chemistry set. At this time she also interested in becoming an engineer. Wahba studied at Cornell University for her undergraduate degree. When she was there women were severely restricted in their privileges, for example she was required to live in a dorm and had a curfew. She received her bachelor's degree from Cornell University in 1956 and a master's degree from the University of Maryland ...
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Mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics with the major subdisciplines of number theory, algebra, geometry, and analysis, respectively. There is no general consensus among mathematicians about a common definition for their academic discipline. Most mathematical activity involves the discovery of properties of abstract objects and the use of pure reason to prove them. These objects consist of either abstractions from nature orin modern mathematicsentities that are stipulated to have certain properties, called axioms. A ''proof'' consists of a succession of applications of deductive rules to already established results. These results include previously proved theorems, axioms, andin case of abstraction from naturesome basic properties that are considered true starting poin ...
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Noisy Data
Noisy data are data that is corrupted, distorted, or has a low signal-to-noise ratio. Improper procedures (or improperly-documented procedures) to subtract out the noise in data can lead to a false sense of accuracy or false conclusions. Noisy data are data with a large amount of additional meaningless information in it called noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured text. Noisy data can adversely affect the results of any data analysis and skew conclusions if not handled properly. Statistical analysis is sometimes used to weed the noise out of noisy data. Sources of noise Differences in real-world measured data from the true values come about from by multiple factors affecting the measurement. Random noise is often a large component of the noise in data.R.Y. Wang, V.C. Storey, C.P. ...
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American Association For The Advancement Of Science
The American Association for the Advancement of Science (AAAS) is an American international non-profit organization with the stated goals of promoting cooperation among scientists, defending scientific freedom, encouraging scientific responsibility, and supporting scientific education and science outreach for the betterment of all humanity. It is the world's largest general scientific society, with over 120,000 members, and is the publisher of the well-known scientific journal ''Science''. History Creation The American Association for the Advancement of Science was created on September 20, 1848, at the Academy of Natural Sciences in Philadelphia, Pennsylvania. It was a reformation of the Association of American Geologists and Naturalists. The society chose William Charles Redfield as their first president because he had proposed the most comprehensive plans for the organization. According to the first constitution which was agreed to at the September 20 meeting, the goal ...
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National Academy Of Sciences
The National Academy of Sciences (NAS) is a United States nonprofit, non-governmental organization. NAS is part of the National Academies of Sciences, Engineering, and Medicine, along with the National Academy of Engineering (NAE) and the National Academy of Medicine (NAM). As a national academy, new members of the organization are elected annually by current members, based on their distinguished and continuing achievements in original research. Election to the National Academy is one of the highest honors in the scientific field. Members of the National Academy of Sciences serve '' pro bono'' as "advisers to the nation" on science, engineering, and medicine. The group holds a congressional charter under Title 36 of the United States Code. Founded in 1863 as a result of an Act of Congress that was approved by Abraham Lincoln, the NAS is charged with "providing independent, objective advice to the nation on matters related to science and technology. ... to provide sci ...
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American Academy Of Arts And Sciences
The American Academy of Arts and Sciences (abbreviation: AAA&S) is one of the oldest learned societies in the United States. It was founded in 1780 during the American Revolution by John Adams, John Hancock, James Bowdoin, Andrew Oliver, and other Founding Fathers of the United States. It is headquartered in Cambridge, Massachusetts. Membership in the academy is achieved through a thorough petition, review, and election process. The academy's quarterly journal, '' Dædalus'', is published by MIT Press on behalf of the academy. The academy also conducts multidisciplinary public policy research. History The Academy was established by the Massachusetts legislature on May 4, 1780, charted in order "to cultivate every art and science which may tend to advance the interest, honor, dignity, and happiness of a free, independent, and virtuous people." The sixty-two incorporating fellows represented varying interests and high standing in the political, professional, and commerc ...
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Grace Wahba 2010
Grace may refer to: Places United States * Grace, Idaho, a city * Grace (CTA station), Chicago Transit Authority's Howard Line, Illinois * Little Goose Creek (Kentucky), location of Grace post office * Grace, Carroll County, Missouri, an unincorporated community * Grace, Laclede County, Missouri, an unincorporated community * Grace, Mississippi, an unincorporated community * Grace, Montana, an unincorporated community * Grace, Hampshire County, West Virginia * Grace, Roane County, West Virginia Elsewhere * Grace (lunar crater), on the Moon * Grace, a crater on Venus People with the name * Grace (given name), a feminine name, including a list of people and fictional characters * Grace (surname), a surname, including a list of people with the name Religion Theory and practice * Grace (prayer), a prayer of thanksgiving said before or after a meal * Divine grace, a theological term present in many religions * Grace in Christianity, the benevolence shown by God toward humanki ...
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Madison, Wisconsin
Madison is the county seat of Dane County, Wisconsin, Dane County and the capital city of the U.S. state of Wisconsin. As of the 2020 United States Census, 2020 census the population was 269,840, making it the second-largest city in Wisconsin by population, after Milwaukee, and the List of United States cities by population, 80th-largest in the U.S. The city forms the core of the Madison, Wisconsin, metropolitan statistical area, Madison Metropolitan Area which includes Dane County and neighboring Iowa County, Wisconsin, Iowa, Green County, Wisconsin, Green, and Columbia County, Wisconsin, Columbia counties for a population of 680,796. Madison is named for American Founding Fathers of the United States, Founding Father and President James Madison. The city is located on the traditional land of the Ho-Chunk, and the Madison area is known as ''Dejope'', meaning "four lakes", or ''Taychopera'', meaning "land of the four lakes", in the Ho-Chunk language. Located on an isthmus and la ...
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Numerical Weather Prediction
Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs. Mathematical models based on the same physical principles can be used to generate either short-term weather forecasts or longer-term climate predictions; the latter are widely applied for understanding and projecting climate change. The improvements made to regional models have allowed for significant improvements in Tropical cyclone track forecasting, tropical cyclone track and air quality forecasts; however, atmospheric models perform poorly at handling process ...
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Medical Imaging
Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues ( physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Although imaging of removed organs and tissues can be performed for medical reasons, such procedures are usually considered part of pathology instead of medical imaging. Measurement and recording techniques that are not primarily designed to produce images, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), and others, represent other technologies that produce data susceptible to representation as a parameter graph versus time or maps that contain data about the measurement ...
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Risk Modeling
Financial risk modeling is the use of formal mathematical and econometric techniques to measure, monitor and control the market risk, credit risk, and operational risk on a firm's balance sheet, on a bank's trading book, or re a fund manager's portfolio value; see Financial risk management. Risk modeling is one of many subtasks within the broader area of financial modeling. Application Risk modeling uses a variety of techniques including market risk, value at risk (VaR), historical simulation (HS), or extreme value theory (EVT) in order to analyze a portfolio and make forecasts of the likely losses that would be incurred for a variety of risks. As above, such risks are typically grouped into credit risk, market risk, model risk, liquidity risk, and operational risk categories. Many large financial intermediary firms use risk modeling to help portfolio managers assess the amount of capital reserves to maintain, and to help guide their purchases and sales of various classes of ...
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DNA Microarrays
A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10−12 moles) of a specific DNA sequence, known as '' probes'' (or ''reporters'' or '' oligos''). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called ''target'') under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981. It was inven ...
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Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making pred ...
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