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πŸ”— Gustav III of Sweden's Coffee Experiment

πŸ”— Medicine πŸ”— Food and drink πŸ”— Sweden πŸ”— Food and drink/Beverages

Gustav III of Sweden's coffee experiment was a twin study ordered by the king to study the health effects of coffee. Although the authenticity of the event has been questioned, the experiment, which was conducted in the second half of the 18th century, failed to prove that coffee was a dangerous beverage.

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πŸ”— Inner-platform effect

πŸ”— Computer science

The inner-platform effect is the tendency of software architects to create a system so customizable as to become a replica, and often a poor replica, of the software development platform they are using. This is generally inefficient and such systems are often considered to be examples of an anti-pattern.

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πŸ”— Absolute Hot

πŸ”— Physics

Absolute hot is a theoretical upper limit to the thermodynamic temperature scale, conceived as an opposite to absolute zero.

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πŸ”— IKEA Effect

πŸ”— Philosophy πŸ”— Psychology

The IKEA effect is a cognitive bias in which consumers place a disproportionately high value on products they partially created. The name derives from the name of Swedish manufacturer and furniture retailer IKEA, which sells many furniture products that require assembly.

The IKEA effect has been described as follows: "The price is low for IKEA products largely because they take labor out of the equation. With a Phillips screwdriver, an Allen wrench and rubber mallet, IKEA customers can very literally build an entire home's worth of furniture on a very tight budget. But what happens when they do?" They "fall in love with their IKEA creations. Even when there are parts missing and the items are incorrectly built, customers in the IKEA study still loved the fruits of their labors."

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πŸ”— Tarski's high school algebra problem

πŸ”— Mathematics

In mathematical logic, Tarski's high school algebra problem was a question posed by Alfred Tarski. It asks whether there are identities involving addition, multiplication, and exponentiation over the positive integers that cannot be proved using eleven axioms about these operations that are taught in high-school-level mathematics. The question was solved in 1980 by Alex Wilkie, who showed that such unprovable identities do exist.

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πŸ”— Cosmological Lithium Problem

πŸ”— Astronomy

In astronomy, the lithium problem or lithium discrepancy refers to the discrepancy between the primordial abundance of lithium as inferred from observations of metal-poor (Population II) halo stars in our galaxy and the amount that should theoretically exist due to Big Bang nucleosynthesis+WMAP cosmic baryon density predictions of the CMB. Namely, the most widely accepted models of the Big Bang suggest that three times as much primordial lithium, in particular lithium-7, should exist. This contrasts with the observed abundance of isotopes of hydrogen (1H and 2H) and helium (3He and 4He) that are consistent with predictions. The discrepancy is highlighted in a so-called "Schramm plot", named in honor of astrophysicist David Schramm, which depicts these primordial abundances as a function of cosmic baryon content from standard BBN predictions.

πŸ”— Dancing Links (A very useful hack by Knuth)

πŸ”— Computing

In computer science, dancing links is a technique for reverting the operation of deleting a node from a circular doubly linked list. It is particularly useful for efficiently implementing backtracking algorithms, such as Donald Knuth's Algorithm X for the exact cover problem. Algorithm X is a recursive, nondeterministic, depth-first, backtracking algorithm that finds all solutions to the exact cover problem. Some of the better-known exact cover problems include tiling, the n queens problem, and Sudoku.

The name dancing links, which was suggested by Donald Knuth, stems from the way the algorithm works, as iterations of the algorithm cause the links to "dance" with partner links so as to resemble an "exquisitely choreographed dance." Knuth credits Hiroshi Hitotsumatsu and Kōhei Noshita with having invented the idea in 1979, but it is his paper which has popularized it.

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πŸ”— Abram Petrovich Gannibal

πŸ”— Biography πŸ”— Russia πŸ”— Russia/technology and engineering in Russia πŸ”— Russia/demographics and ethnography of Russia πŸ”— African diaspora πŸ”— Russia/Russian, Soviet, and CIS military history πŸ”— Russia/history of Russia

Abram Petrovich Gannibal, also Hannibal or Ganibal, or Abram Hannibal or Abram Petrov (Russian: Абра́м ΠŸΠ΅Ρ‚Ρ€ΠΎΜΠ²ΠΈΡ‡ Ганниба́л; c. 1696 – 14 May 1781), was a Russian military engineer, major-general, and nobleman of African origin. Kidnapped as a child, Gannibal was taken to Russia and presented as a gift to Peter the Great, where he was freed, adopted and raised in the Emperor's court household as his godson.

Gannibal eventually rose to become a prominent member of the imperial court in the reign of Peter's daughter Elizabeth. He had 11 children, most of whom became members of the Russian nobility; he was a great-grandfather of the author and poet Alexander Pushkin.

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πŸ”— WinFS

πŸ”— Computing πŸ”— Microsoft Windows πŸ”— Microsoft Windows/Computing πŸ”— Computing/Software πŸ”— Microsoft

WinFS (short for Windows Future Storage) was the code name for a canceled data storage and management system project based on relational databases, developed by Microsoft and first demonstrated in 2003 as an advanced storage subsystem for the Microsoft Windows operating system, designed for persistence and management of structured, semi-structured and unstructured data.

WinFS includes a relational database for storage of information, and allows any type of information to be stored in it, provided there is a well defined schema for the type. Individual data items could then be related together by relationships, which are either inferred by the system based on certain attributes or explicitly stated by the user. As the data has a well defined schema, any application can reuse the data; and using the relationships, related data can be effectively organized as well as retrieved. Because the system knows the structure and intent of the information, it can be used to make complex queries that enable advanced searching through the data and aggregating various data items by exploiting the relationships between them.

While WinFS and its shared type schema make it possible for an application to recognize the different data types, the application still has to be coded to render the different data types. Consequently, it would not allow development of a single application that can view or edit all data types; rather, what WinFS enables applications to do is understand the structure of all data and extract the information that they can use further. When WinFS was introduced at the 2003 Professional Developers Conference, Microsoft also released a video presentation, named IWish, showing mockup interfaces that showed how applications would expose interfaces that take advantage of a unified type system. The concepts shown in the video ranged from applications using the relationships of items to dynamically offer filtering options to applications grouping multiple related data types and rendering them in a unified presentation.

WinFS was billed as one of the pillars of the "Longhorn" wave of technologies, and would ship as part of the next version of Windows. It was subsequently decided that WinFS would ship after the release of Windows Vista, but those plans were shelved in June 2006, with some of its component technologies being integrated into ADO.NET and Microsoft SQL Server.

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  • "WinFS" | 2022-02-18 | 23 Upvotes 2 Comments

πŸ”— Eigenface

πŸ”— Robotics

An eigenface () is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification. The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.

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