Civil Rights
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In August 1908, a white mob attacked Springfield, Illinois’s Black community, destroying homes and businesses and lynching two Black men. National outrage over the violence helped spur the movement that created the NAACP the following year.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. (May 2020) |
In theoretical computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation. Markov algorithms are named after the Soviet mathematician Andrey Markov, Jr.
Refal is a programming language based on Markov algorithms.
Normal algorithms are verbal, that is, intended to be applied to strings in different alphabets.
The definition of any normal algorithm consists of two parts: an alphabet, which is a set of symbols, and a scheme. The algorithm is applied to strings of symbols of the alphabet. The scheme is a finite ordered set of substitution formulas. Each formula can be either simple or final. Simple substitution formulas are represented by strings of the form , where and are two arbitrary strings in the alphabet. Similarly, final substitution formulas are represented by strings of the form .
Here is an example of a normal algorithm scheme in the five-letter alphabet :
The process of applying the normal algorithm to an arbitrary string in the alphabet of this algorithm is a discrete sequence of elementary steps, consisting of the following. Let’s assume that is the word obtained in the previous step of the algorithm (or the original word , if the current step is the first). If of the substitution formulas there is no left-hand side which is included in the , then the algorithm terminates, and the result of its work is considered to be the string . Otherwise, the first of the substitution formulae whose left sides are included in is selected. If the substitution formula is of the form , then out of all of possible representations of the string of the form (where and are arbitrary strings) the one with the shortest is chosen. Then the algorithm terminates and the result of its work is considered to be . However, if this substitution formula is of the form , then out of all of the possible representations of the string of the form of the one with the shortest is chosen, after which the string is considered to be the result of the current step, subject to further processing in the next step.
For example, the process of applying the algorithm described above to the word results in the sequence of words , , , , , , , , , and , after which the algorithm stops with the result .
For other examples, see below.
Any normal algorithm is equivalent to some Turing machine, and vice versa – any Turing machine is equivalent to some normal algorithm. A version of the Church–Turing thesis formulated in relation to the normal algorithm is called the "principle of normalization."
Normal algorithms have proved to be a convenient means for the construction of many sections of constructive mathematics. Moreover, inherent in the definition of a normal algorithm are a number of ideas used in programming languages aimed at handling symbolic information – for example, in Refal.
The Rules are a sequence of pairs of strings, usually presented in the form of pattern → replacement. Each rule may be either ordinary or terminating.
Given an input string:
Note that after each rule application the search starts over from the first rule.
The following example shows the basic operation of a Markov algorithm.
"I bought a B of As from T S."
If the algorithm is applied to the above example, the Symbol string will change in the following manner.
The algorithm will then terminate.
These rules give a more interesting example. They rewrite binary numbers to their unary counterparts. For example, 101 will be rewritten to a string of 5 consecutive bars.
"101"
If the algorithm is applied to the above example, it will terminate after the following steps.
Source: Wikipedia. Article content is retrieved live through the MediaWiki API.
In theoretical computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been shown to be Turing-complete, which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation. Markov algorithms are named after the Soviet mathematician Andrey Markov, Jr. Refal is a programming language based on Markov algorithms.
Andrey Andreyevich Markov (Russian: Андре́й Андре́евич Ма́рков; 22 September 1903, Saint Petersburg – 11 October 1979, Moscow) was a Soviet mathematician, the son of the Russian mathematician Andrey Markov Sr, and one of the key founders of the Russian school of constructive mathematics and logic. He made outstanding contributions to various areas of mathematics, including differential equations, topology, mathematical logic and the foundations of mathematics. His name is in particular associated with Markov's principle and Markov's rule in mathematical logic, Markov's theorem in knot theory and Markov algorithm in theoretical computer science. An important result that he proved in 1947 was that the word problem for semigroups was unsolvable; Emil Leon Post obtained the same result independently at about the same time. In 1953 he became a member of the Communist Party. In 1960, Markov obtained fundamental results showing that the classification of four-dimensional manifolds is undecidable: no general algorithm exists for distinguishing two arbitrary manifolds with four or more dimensions. This is because four-dimensional manifolds have sufficient flexibility to allow us to embed any algorithm within their structure. Hence, classifying all four-manifolds would imply a solution to Turing's halting problem. Embedding implies failure to create a correspondence between algorithms and indexing (naturally uncountably infinite, but even larger) of the four-manifolds structure. Failure is in Cantor's sense. Indexing is in Godel's sense. His doctoral students include Boris Kushner, Gennady Makanin, and Nikolai Shanin.
In statistics and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution from which direct sampling is difficult. New samples are added to the sequence in two steps: first a new sample is proposed based on the previous sample, then the proposed sample is either added to the sequence or rejected depending on the value of the probability distribution at that point. The resulting sequence can be used to approximate the distribution (e.g. to generate a histogram) or to compute an integral (e.g. an expected value). Metropolis–Hastings and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, there are usually other methods (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and these are free from the problem of autocorrelated samples that is inherent in MCMC methods.
The forward–backward algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions o 1 : T := o 1 , … , o T {\displaystyle o_{1:T}:=o_{1},\dots ,o_{T}} , i.e. it computes, for all hidden state variables X t ∈ { X 1 , … , X T } {\displaystyle X_{t}\in \{X_{1},\dots ,X_{T}\}} , the distribution P ( X t | o 1 : T ) {\displaystyle P(X_{t}\ |\ o_{1:T})} . This inference task is usually called smoothing. The algorithm makes use of the principle of dynamic programming to efficiently compute the values that are required to obtain the posterior marginal distributions in two passes. The first pass goes forward in time while the second goes backward in time; hence the name forward–backward algorithm. The term forward–backward algorithm is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forward–backward manner. In this sense, the descriptions in the remainder of this article refer only to one specific instance of this class.
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Shirley Chisholm, elected in 1968.