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https://en.wikipedia.org/wiki/Type%20system | In computer programming, a type system is a logical system comprising a set of rules that assigns a property called a type (for example, integer, floating point, string) to every term (a word, phrase, or other set of symbols). Usually the terms are various language constructs of a computer program, such as variables, expressions, functions, or modules. A type system dictates the operations that can be performed on a term. For variables, the type system determines the allowed values of that term. Type systems formalize and enforce the otherwise implicit categories the programmer uses for algebraic data types, data structures, or other components (e.g. "string", "array of float", "function returning boolean").
Type systems are often specified as part of programming languages and built into interpreters and compilers, although the type system of a language can be extended by optional tools that perform added checks using the language's original type syntax and grammar. The main purpose of a type system in a programming language is to reduce possibilities for bugs in computer programs due to type errors. The given type system in question determines what constitutes a type error, but in general, the aim is to prevent operations expecting a certain kind of value from being used with values of which that operation does not make sense (validity errors). Type systems allow defining interfaces between different parts of a computer program, and then checking that the parts have been connected in a consistent way. This checking can happen statically (at compile time), dynamically (at run time), or as a combination of both. Type systems have other purposes as well, such as expressing business rules, enabling certain compiler optimizations, allowing for multiple dispatch, and providing a form of documentation.
Usage overview
An example of a simple type system is that of the C language. The portions of a C program are the function definitions. One function is invoked by another |
https://en.wikipedia.org/wiki/String%20literal | A string literal or anonymous string is a literal for a string value in the source code of a computer program. Modern programming languages commonly use a quoted sequence of characters, formally "bracketed delimiters", as in x = "foo", where "foo" is a string literal with value foo. Methods such as escape sequences can be used to avoid the problem of delimiter collision (issues with brackets) and allow the delimiters to be embedded in a string. There are many alternate notations for specifying string literals especially in complicated cases. The exact notation depends on the programming language in question. Nevertheless, there are general guidelines that most modern programming languages follow.
Syntax
Bracketed delimiters
Most modern programming languages use bracket delimiters (also balanced delimiters)
to specify string literals. Double quotations are the most common quoting delimiters used:
"Hi There!"
An empty string is literally written by a pair of quotes with no character at all in between:
""
Some languages either allow or mandate the use of single quotations instead of double quotations (the string must begin and end with the same kind of quotation mark and the type of quotation mark may or may not give slightly different semantics):
'Hi There!'
These quotation marks are unpaired (the same character is used as an opener and a closer), which is a hangover from the typewriter technology which was the precursor of the earliest computer input and output devices.
In terms of regular expressions, a basic quoted string literal is given as:
"[^"]*"
This means that a string literal is written as: a quote, followed by zero, one, or more non-quote characters, followed by a quote. In practice this is often complicated by escaping, other delimiters, and excluding newlines.
Paired delimiters
A number of languages provide for paired delimiters, where the opening and closing delimiters are different. These also often allow nested strings, so delimiters |
https://en.wikipedia.org/wiki/Angular%20frequency | In physics, angular frequency (symbol ω), also called angular speed and angular rate, is a scalar measure of the angle rate (the angle per unit time) or the temporal rate of change of the phase argument of a sinusoidal waveform or sine function (for example, in oscillations and waves).
Angular frequency (or angular speed) is the magnitude of the pseudovector quantity angular velocity.
Angular frequency can be obtained multiplying rotational frequency, ν (or ordinary frequency, f) by a full turn (2 radians): ω2radν.
It can also be formulated as ωdθ/dt, the instantaneous rate of change of the angular displacement, θ, with respect to time, t.
Units
In SI units, angular frequency is normally presented in radians per second, even when it does not express a rotational value. The unit hertz (Hz) is dimensionally equivalent, but by convention it is only used for frequency f, never for angular frequency ω. This convention is used to help avoid the confusion that arises when dealing with quantities such as frequency and angular quantities because the units of measure (such as cycle or radian) are considered to be one and hence may be omitted when expressing quantities in SI units.
In digital signal processing, the frequency may be normalized by the sampling rate, yielding the normalized frequency.
Examples
Circular motion
In a rotating or orbiting object, there is a relation between distance from the axis, , tangential speed, , and the angular frequency of the rotation. During one period, , a body in circular motion travels a distance . This distance is also equal to the circumference of the path traced out by the body, . Setting these two quantities equal, and recalling the link between period and angular frequency we obtain:
Oscillations of a spring
An object attached to a spring can oscillate. If the spring is assumed to be ideal and massless with no damping, then the motion is simple and harmonic with an angular frequency given by
where
k is the spring consta |
https://en.wikipedia.org/wiki/Alternating%20caps | Alternating caps, also known as studly caps or sticky caps (where "caps" is short for capital letters), is a form of text notation in which the capitalization of letters varies by some pattern, or arbitrarily (often also omitting spaces between words and occasionally some letters), such as "aLtErNaTiNg cApS", "sTuDlY cApS" or "sTiCKycApS".
History
According to the Jargon File, the origin and significance of the practice is obscure. The term "alternating case" has been used as early as the 1970s, in several studies on word identification.
Arbitrary variation found popularity among adolescent users during the BBS and early WWW eras of online culture, as if in parody of the marginally less idiosyncratic capitalization found in common trade and service marks of the time. This method was extensively used since the 1980s in the BBS-world and warez scene (for example in FILE_ID.DIZ and .nfo files) to show "elite" (or elitist) attitude, the often used variant was using small-caps vowels and capitalised consonants ("THiS iS aN eXCePTioNaLLy eLiTe SeNTeNCe.") or reversed capitals ("eXTENDED kEY gENERERATOR pRO").
The iNiQUiTY BBS software based on Renegade had a feature to support two variants of this automatically: either all vowels would be uppercase or all vowels would be lowercase, with the consonants as the other case.
A meme known as "Mocking SpongeBob" popularized using alternating caps to convey a mocking tone starting in May 2017, leading to alternating caps becoming a mainstream method of conveying mockery in text.
Usage and effect
Alternating caps are typically used to display mockery in text messages.
The randomized capitalization leads to the flow of words being broken, making it harder for the text to be read as it disrupts word identification even when the size of the letters is the same as in uppercase or lowercase.
Unlike the use of all-lowercase letters, which suggests laziness as a motivation, alternating caps requires additional effort to type, ei |
https://en.wikipedia.org/wiki/Binomial%20type | In mathematics, a polynomial sequence, i.e., a sequence of polynomials indexed by non-negative integers in which the index of each polynomial equals its degree, is said to be of binomial type if it satisfies the sequence of identities
Many such sequences exist. The set of all such sequences forms a Lie group under the operation of umbral composition, explained below. Every sequence of binomial type may be expressed in terms of the Bell polynomials. Every sequence of binomial type is a Sheffer sequence (but most Sheffer sequences are not of binomial type). Polynomial sequences put on firm footing the vague 19th century notions of umbral calculus.
Examples
In consequence of this definition the binomial theorem can be stated by saying that the sequence is of binomial type.
The sequence of "lower factorials" is defined by(In the theory of special functions, this same notation denotes upper factorials, but this present usage is universal among combinatorialists.) The product is understood to be 1 if n = 0, since it is in that case an empty product. This polynomial sequence is of binomial type.
Similarly the "upper factorials"are a polynomial sequence of binomial type.
The Abel polynomialsare a polynomial sequence of binomial type.
The Touchard polynomialswhere is the number of partitions of a set of size into disjoint non-empty subsets, is a polynomial sequence of binomial type. Eric Temple Bell called these the "exponential polynomials" and that term is also sometimes seen in the literature. The coefficients are "Stirling numbers of the second kind". This sequence has a curious connection with the Poisson distribution: If is a random variable with a Poisson distribution with expected value then . In particular, when , we see that the th moment of the Poisson distribution with expected value is the number of partitions of a set of size , called the th Bell number. This fact about the th moment of that particular Poisson distribution is "Dobinski's |
https://en.wikipedia.org/wiki/Cardinal%20direction | The four cardinal directions, or cardinal points, are the four main compass directions: north, south, east, and west, commonly denoted by their initials N, S, E, and W respectively. Relative to north, the directions east, south, and west are at 90 degree intervals in the clockwise direction.
The ordinal directions (also called the intercardinal directions) are northeast (NE), southeast (SE), southwest (SW), and northwest (NW). The intermediate direction of every set of intercardinal and cardinal direction is called a secondary intercardinal direction. These eight shortest points in the compass rose shown to the right are:
West-northwest (WNW)
North-northwest (NNW)
North-northeast (NNE)
East-northeast (ENE)
East-southeast (ESE)
South-southeast (SSE)
South-southwest (SSW)
West-southwest (WSW)
Points between the cardinal directions form the points of the compass. Arbitrary horizontal directions may be indicated by their azimuth angle value.
Determination
Additional points
Azimuth
The directional names are routinely associated with azimuths, the angle of rotation (in degrees) in the unit circle over the horizontal plane. It is a necessary step for navigational calculations (derived from trigonometry) and for use with Global Positioning System (GPS) receivers. The four cardinal directions correspond to the following degrees of a compass:
North (N): 0° = 360°
East (E): 90°
South (S): 180°
West (W): 270°
Intercardinal directions
The intercardinal (intermediate, or, historically, ordinal) directions are the four intermediate compass directions located halfway between each pair of cardinal directions.
Northeast (NE), 45°, halfway between north and east, is the opposite of southwest.
Southeast (SE), 135°, halfway between south and east, is the opposite of northwest.
Southwest (SW), 225°, halfway between south and west, is the opposite of northeast.
Northwest (NW), 315°, halfway between north and west, is the opposite of southeast.
These eight directi |
https://en.wikipedia.org/wiki/Quadratic%20residue | In number theory, an integer q is called a quadratic residue modulo n if it is congruent to a perfect square modulo n; i.e., if there exists an integer x such that:
Otherwise, q is called a quadratic nonresidue modulo n.
Originally an abstract mathematical concept from the branch of number theory known as modular arithmetic, quadratic residues are now used in applications ranging from acoustical engineering to cryptography and the factoring of large numbers.
History, conventions, and elementary facts
Fermat, Euler, Lagrange, Legendre, and other number theorists of the 17th and 18th centuries established theorems and formed conjectures about quadratic residues, but the first systematic treatment is § IV of Gauss's Disquisitiones Arithmeticae (1801). Article 95 introduces the terminology "quadratic residue" and "quadratic nonresidue", and states that if the context makes it clear, the adjective "quadratic" may be dropped.
For a given n a list of the quadratic residues modulo n may be obtained by simply squaring the numbers 0, 1, ..., . Because a2 ≡ (n − a)2 (mod n), the list of squares modulo n is symmetric around n/2, and the list only needs to go that high. This can be seen in the table below.
Thus, the number of quadratic residues modulo n cannot exceed n/2 + 1 (n even) or (n + 1)/2 (n odd).
The product of two residues is always a residue.
Prime modulus
Modulo 2, every integer is a quadratic residue.
Modulo an odd prime number p there are (p + 1)/2 residues (including 0) and (p − 1)/2 nonresidues, by Euler's criterion. In this case, it is customary to consider 0 as a special case and work within the multiplicative group of nonzero elements of the field Z/pZ. (In other words, every congruence class except zero modulo p has a multiplicative inverse. This is not true for composite moduli.)
Following this convention, the multiplicative inverse of a residue is a residue, and the inverse of a nonresidue is a nonresidue.
Following this convention, modulo an od |
https://en.wikipedia.org/wiki/Jenga | Jenga is a game of physical skill created by British board game designer and author Leslie Scott and marketed by Hasbro. Players take turns removing one block at a time from a tower constructed of 54 blocks. Each block removed is then placed on top of the tower, creating a progressively more unstable structure.
Rules
Jenga is played with 54 wooden blocks. Each block is three times as long as it is wide, and one fifth as thick as its length – . Blocks have small, random variations from these dimensions so as to create imperfections in the stacking process and make the game more challenging. To begin the game, the blocks are stacked into a solid rectangular tower of 18 layers, with three blocks per layer. The blocks within each layer are oriented in the same direction, with their long sides touching, and are perpendicular to the ones in the layer immediately below. A plastic tray provided with the game can be used to assist in setup.
Starting with the one who built the tower, players take turns removing one block from any level below the highest completed one and placing it horizontally atop the tower, perpendicular to any blocks on which it is to rest. Each player may use only one hand to touch the tower or move a block at any given time, but may switch hands whenever desired. Once a level contains three blocks, it is complete and may not have any more blocks added to it. A block may be touched or nudged to determine whether it is loose enough to remove without disturbing the rest of the tower, but it must be returned to its original position if the player decides to move a different one. A turn ends when the next player in sequence touches the tower or when 10 seconds have elapsed since the placement of a block, whichever occurs first.
The game ends when any portion of the tower collapses, caused by either the removal of a block or its new placement. The last player to complete a turn before the collapse is the winner.
Origins
Jenga was created by Leslie Scott, |
https://en.wikipedia.org/wiki/Chromatid | A chromatid (Greek khrōmat- 'color' + -id) is one half of a duplicated chromosome. Before replication, one chromosome is composed of one DNA molecule. In replication, the DNA molecule is copied, and the two molecules are known as chromatids. During the later stages of cell division these chromatids separate longitudinally to become individual chromosomes.
Chromatid pairs are normally genetically identical, and said to be homozygous. However, if mutations occur, they will present slight differences, in which case they are heterozygous. The pairing of chromatids should not be confused with the ploidy of an organism, which is the number of homologous versions of a chromosome.
Sister chromatids
Chromatids may be sister or non-sister chromatids. A sister chromatid is either one of the two chromatids of the same chromosome joined together by a common centromere. A pair of sister chromatids is called a dyad. Once sister chromatids have separated (during the anaphase of mitosis or the anaphase II of meiosis during sexual reproduction), they are again called chromosomes, each having the same genetic mass as one of the individual chromatids that made up its parent. The DNA sequence of two sister chromatids is completely identical (apart from very rare DNA copying errors).
Sister chromatid exchange (SCE) is the exchange of genetic information between two sister chromatids. SCEs can occur during mitosis or meiosis. SCEs appear to primarily reflect DNA recombinational repair processes responding to DNA damage (see articles Sister chromatids and Sister chromatid exchange).
Non-sister chromatids, on the other hand, refers to either of the two chromatids of paired homologous chromosomes, that is, the pairing of a paternal chromosome and a maternal chromosome. In chromosomal crossovers, non-sister (homologous) chromatids form chiasmata to exchange genetic material during the prophase I of meiosis (See Homologous chromosome pair).
See also
Kinetochore
References
Chromosomes |
https://en.wikipedia.org/wiki/Rolle%27s%20theorem | In calculus, Rolle's theorem or Rolle's lemma essentially states that any real-valued differentiable function that attains equal values at two distinct points must have at least one stationary point somewhere between them—that is, a point where the first derivative (the slope of the tangent line to the graph of the function) is zero. The theorem is named after Michel Rolle.
Standard version of the theorem
If a real-valued function is continuous on a proper closed interval , differentiable on the open interval , and , then there exists at least one in the open interval such that
This version of Rolle's theorem is used to prove the mean value theorem, of which Rolle's theorem is indeed a special case. It is also the basis for the proof of Taylor's theorem.
History
Although the theorem is named after Michel Rolle, Rolle's 1691 proof covered only the case of polynomial functions. His proof did not use the methods of differential calculus, which at that point in his life he considered to be fallacious. The theorem was first proved by Cauchy in 1823 as a corollary of a proof of the mean value theorem. The name "Rolle's theorem" was first used by Moritz Wilhelm Drobisch of Germany in 1834 and by Giusto Bellavitis of Italy in 1846.
Examples
First example
For a radius , consider the function
Its graph is the upper semicircle centered at the origin. This function is continuous on the closed interval and differentiable in the open interval , but not differentiable at the endpoints and . Since , Rolle's theorem applies, and indeed, there is a point where the derivative of is zero. The theorem applies even when the function cannot be differentiated at the endpoints because it only requires the function to be differentiable in the open interval.
Second example
If differentiability fails at an interior point of the interval, the conclusion of Rolle's theorem may not hold. Consider the absolute value function
Then , but there is no between −1 and 1 for which t |
https://en.wikipedia.org/wiki/Transitive%20relation | In mathematics, a relation on a set is transitive if, for all elements , , in , whenever relates to and to , then also relates to . Each partial order as well as each equivalence relation needs to be transitive.
Definition
A homogeneous relation on the set is a transitive relation if,
for all , if and , then .
Or in terms of first-order logic:
,
where is the infix notation for .
Examples
As a non-mathematical example, the relation "is an ancestor of" is transitive. For example, if Amy is an ancestor of Becky, and Becky is an ancestor of Carrie, then Amy, too, is an ancestor of Carrie.
On the other hand, "is the birth parent of" is not a transitive relation, because if Alice is the birth parent of Brenda, and Brenda is the birth parent of Claire, then this does not imply that Alice is the birth parent of Claire. What is more, it is antitransitive: Alice can never be the birth parent of Claire.
Non-transitive, non-antitransitive relations include sports fixtures (playoff schedules), 'knows' and 'talks to'.
"Is greater than", "is at least as great as", and "is equal to" (equality) are transitive relations on various sets, for instance, the set of real numbers or the set of natural numbers:
whenever x > y and y > z, then also x > z
whenever x ≥ y and y ≥ z, then also x ≥ z
whenever x = y and y = z, then also x = z.
More examples of transitive relations:
"is a subset of" (set inclusion, a relation on sets)
"divides" (divisibility, a relation on natural numbers)
"implies" (implication, symbolized by "⇒", a relation on propositions)
Examples of non-transitive relations:
"is the successor of" (a relation on natural numbers)
"is a member of the set" (symbolized as "∈")
"is perpendicular to" (a relation on lines in Euclidean geometry)
The empty relation on any set is transitive because there are no elements such that and , and hence the transitivity condition is vacuously true. A relation containing only one ordered pair is also transitiv |
https://en.wikipedia.org/wiki/Automotive%20aerodynamics | Automotive aerodynamics is the study of the aerodynamics of road vehicles. Its main goals are reducing drag and wind noise, minimizing noise emission, and preventing undesired lift forces and other causes of aerodynamic instability at high speeds. Air is also considered a fluid in this case. For some classes of racing vehicles, it may also be important to produce downforce to improve traction and thus cornering abilities.
History
The frictional force of aerodynamic drag increases significantly with vehicle speed. As early as the 1920s engineers began to consider automobile shape in reducing aerodynamic drag at higher speeds. By the 1950s German and British automotive engineers were systematically analyzing the effects of automotive drag for the higher performance vehicles. By the late 1960s scientists also became aware of the significant increase in sound levels emitted by automobiles at high speed. These effects were understood to increase the intensity of sound levels for adjacent land uses at a non-linear rate. Soon highway engineers began to design roadways to consider the speed effects of aerodynamic drag produced sound levels, and automobile manufacturers considered the same factors in vehicle design.
Features of aerodynamic vehicles
An aerodynamic automobile will integrate the wheel arcs and headlights to reduce wind resistance into the overall shape to also reduce drag. It will be streamlined; for example, it does not have sharp edges crossing the wind stream above the windshield and will feature a sort of tail called a fastback or Kammback or liftback.
Note that the Aptera 2e, the Loremo, and the Volkswagen XL1 try to reduce the area of their back.
It will have a flat and smooth floor to support the Venturi effect and produce desirable downwards aerodynamic forces. The air that rams into the engine bay, is used for cooling, combustion, and for passengers, then reaccelerated by a nozzle and then ejected under the floor.
For mid and rear engines air |
https://en.wikipedia.org/wiki/Selective%20breeding | Selective breeding (also called artificial selection) is the process by which humans use animal breeding and plant breeding to selectively develop particular phenotypic traits (characteristics) by choosing which typically animal or plant males and females will sexually reproduce and have offspring together. Domesticated animals are known as breeds, normally bred by a professional breeder, while domesticated plants are known as varieties, cultigens, cultivars, or breeds. Two purebred animals of different breeds produce a crossbreed, and crossbred plants are called hybrids. Flowers, vegetables and fruit-trees may be bred by amateurs and commercial or non-commercial professionals: major crops are usually the provenance of the professionals.
In animal breeding artificial selection is often combined with techniques such as inbreeding, linebreeding, and outcrossing. In plant breeding, similar methods are used. Charles Darwin discussed how selective breeding had been successful in producing change over time in his 1859 book, On the Origin of Species. Its first chapter discusses selective breeding and domestication of such animals as pigeons, cats, cattle, and dogs. Darwin used artificial selection as an analogy to propose and explain the theory of natural selection but distinguished the latter from the former as a separate process that is non-directed.
The deliberate exploitation of selective breeding to produce desired results has become very common in agriculture and experimental biology.
Selective breeding can be unintentional, for example, resulting from the process of human cultivation; and it may also produce unintended – desirable or undesirable – results. For example, in some grains, an increase in seed size may have resulted from certain ploughing practices rather than from the intentional selection of larger seeds. Most likely, there has been an interdependence between natural and artificial factors that have resulted in plant domestication.
History
Selective |
https://en.wikipedia.org/wiki/Web%20container | A web container (also known as a servlet container;
and compare "webcontainer") is the component of a web server that interacts with Jakarta Servlets. A web container is responsible for managing the lifecycle of servlets, mapping a URL to a particular servlet and ensuring that the URL requester has the correct access-rights. A web container handles requests to servlets, Jakarta Server Pages (JSP) files, and other types of files that include server-side code. The Web container creates servlet instances, loads and unloads servlets, creates and manages request and response objects, and performs other servlet-management tasks. A web container implements the web component contract of the Jakarta EE architecture. This architecture specifies a runtime environment for additional web components, including security, concurrency, lifecycle management, transaction, deployment, and other services.
List of Servlet containers
The following is a list of applications which implement the Jakarta Servlet specification from Eclipse Foundation, divided depending on whether they are directly sold or not.
Open source Web containers
Apache Tomcat (formerly Jakarta Tomcat) is an open source web container available under the Apache Software License.
Apache Tomcat 6 and above are operable as general application container (prior versions were web containers only)
Apache Geronimo is a full Java EE 6 implementation by Apache Software Foundation.
Enhydra, from Lutris Technologies.
GlassFish from Eclipse Foundation (an application server, but includes a web container).
Jaminid contains a higher abstraction than servlets.
Jetty, from the Eclipse Foundation. Also supports SPDY and WebSocket protocols.
Payara is another application server, derived from Glassfish.
Winstone supports specification v2.5 as of 0.9, has a focus on minimal configuration and the ability to strip the container down to only what you need.
Tiny Java Web Server (TJWS) 2.5 , small footprint, modular design.
Virgo f |
https://en.wikipedia.org/wiki/Barycenter%20%28astronomy%29 | In astronomy, the barycenter (or barycentre; ) is the center of mass of two or more bodies that orbit one another and is the point about which the bodies orbit. A barycenter is a dynamical point, not a physical object. It is an important concept in fields such as astronomy and astrophysics. The distance from a body's center of mass to the barycenter can be calculated as a two-body problem.
If one of the two orbiting bodies is much more massive than the other and the bodies are relatively close to one another, the barycenter will typically be located within the more massive object. In this case, rather than the two bodies appearing to orbit a point between them, the less massive body will appear to orbit about the more massive body, while the more massive body might be observed to wobble slightly. This is the case for the Earth–Moon system, whose barycenter is located on average from Earth's center, which is 75% of Earth's radius of . When the two bodies are of similar masses, the barycenter will generally be located between them and both bodies will orbit around it. This is the case for Pluto and Charon, one of Pluto's natural satellites, as well as for many binary asteroids and binary stars. When the less massive object is far away, the barycenter can be located outside the more massive object. This is the case for Jupiter and the Sun; despite the Sun being a thousandfold more massive than Jupiter, their barycenter is slightly outside the Sun due to the relatively large distance between them.
In astronomy, barycentric coordinates are non-rotating coordinates with the origin at the barycenter of two or more bodies. The International Celestial Reference System (ICRS) is a barycentric coordinate system centered on the Solar System's barycenter.
Two-body problem
The barycenter is one of the foci of the elliptical orbit of each body. This is an important concept in the fields of astronomy and astrophysics. In a simple two-body case, the distance from the center o |
https://en.wikipedia.org/wiki/Collision%20domain | A collision domain is a network segment connected by a shared medium or through repeaters where simultaneous data transmissions collide with one another. The collision domain applies particularly in wireless networks, but also affected early versions of Ethernet. A network collision occurs when more than one device attempts to send a packet on a network segment at the same time. Members of a collision domain may be involved in collisions with one another. Devices outside the collision domain do not have collisions with those inside.
A channel access method dictates that only one device in the collision domain may transmit at any one time, and the other devices in the domain listen to the network and refrain from transmitting while others are already transmitting in order to avoid collisions. Because only one device may be transmitting at any one time, total network bandwidth is shared among all devices on the collision domain. Collisions also decrease network efficiency in a collision domain as collisions require devices to abort transmission and retransmit at a later time.
Since data bits are propagated at a finite speed, simultaneously is to be defined in terms of the size of the collision domain and the minimum packet size allowed. A smaller packet size or a larger dimension would make it possible for a sender to finish sending the packet without the first bits of the message being able to reach the most remote node. So, that node could start sending as well, without a clue to the transmission already taking place and destroying the first packet. Unless the size of the collision domain allows the initial sender to receive the second transmission attempt – the collision – within the time it takes to send the packet, they would neither be able to detect the collision nor to repeat the transmission – this is called a late collision.
Ethernet
On Ethernet using shared media, collisions are resolved using carrier-sense multiple access with collision detection (CSMA/ |
https://en.wikipedia.org/wiki/Maze%20generation%20algorithm | Maze generation algorithms are automated methods for the creation of mazes.
Graph theory based methods
A maze can be generated by starting with a predetermined arrangement of cells (most commonly a rectangular grid but other arrangements are possible) with wall sites between them. This predetermined arrangement can be considered as a connected graph with the edges representing possible wall sites and the nodes representing cells. The purpose of the maze generation algorithm can then be considered to be making a subgraph in which it is challenging to find a route between two particular nodes.
If the subgraph is not connected, then there are regions of the graph that are wasted because they do not contribute to the search space. If the graph contains loops, then there may be multiple paths between the chosen nodes. Because of this, maze generation is often approached as generating a random spanning tree. Loops, which can confound naive maze solvers, may be introduced by adding random edges to the result during the course of the algorithm.
The animation shows the maze generation steps for a
graph that is not on a rectangular grid.
First, the computer creates a random planar graph G
shown in blue, and its dual F
shown in yellow. Second, the computer traverses F using a chosen
algorithm, such as a depth-first search, coloring the path red.
During the traversal, whenever a red edge crosses over a blue edge,
the blue edge is removed.
Finally, when all vertices of F have been visited, F is erased
and two edges from G, one for the entrance and one for the exit, are removed.
Randomized depth-first search
This algorithm, also known as the "recursive backtracker" algorithm, is a randomized version of the depth-first search algorithm.
Frequently implemented with a stack, this approach is one of the simplest ways to generate a maze using a computer. Consider the space for a maze being a large grid of cells (like a large chess board), each cell starting with four wall |
https://en.wikipedia.org/wiki/AN/USQ-20 | The AN/USQ-20, or CP-642 or Naval Tactical Data System (NTDS), was designed as a more reliable replacement for the Seymour Cray-designed AN/USQ-17 with the same instruction set. The first batch of 17 computers were delivered to the Navy starting in early 1961.
A version of the AN/USQ-20 for use by the other military services and NASA was designated the UNIVAC 1206. Another version, designated the G-40, replaced the vacuum tube UNIVAC 1104 in the BOMARC Missile Program.
Technical
The machine was the size and shape of an old-fashioned double-door refrigerator, about six feet tall (roughly 1.80 meters).
Instructions were represented as 30-bit words in the following format:
f 6 bits function code
j 3 bits jump condition designator
k 3 bits partial word designator
b 3 bits which index register to use
y 15 bits operand address in memory
Numbers were represented as 30-bit words. This allowed for five 6-bit alphanumeric characters per word.
The main memory was 32,768 words of core memory.
The available processor registers were:
One 30-bit arithmetic (A) register.
A contiguous 30-bit Q register (total of 60 bits for the result of multiplication or the dividend in division).
Seven 15-bit index (B) registers (note: register B0 is always zero).
See also
CMS-2
List of UNIVAC products
History of computing hardware
Military computers
References
External links
UNIVAC-NTDS: UNIVAC 1206, AN/USQ-20 – From the Antique Computer website
UNIVAC hardware
Transistorized computers
Military computers
Avionics computers
Military electronics of the United States |
https://en.wikipedia.org/wiki/Thin-film%20memory | Thin-film memory is a high-speed alternative to magnetic-core memory developed by Sperry Rand in a government-funded research project.
Instead of threading individual ferrite cores on wires, thin-film memory consisted of 4-micrometer thick dots of permalloy, an iron–nickel alloy, deposited on small glass plates by vacuum evaporation techniques and a mask. The drive and sense lines were then added using printed circuit wiring over the alloy dots. This provided very fast access times in the range of 670 nanoseconds, but was very expensive to produce.
In 1962, the UNIVAC 1107, intended for the civilian marketplace, used thin-film memory only for its 128-word general register stack. Military computers, where cost was less of a concern, used larger amounts of thin-film memory. Thin film was also used in a number of high-speed computer projects, including the high-end of the IBM System/360 line, but general advances in core tended to keep pace.
External links
Computer memory
Non-volatile memory
Thin films |
https://en.wikipedia.org/wiki/AN/UYK-8 | The AN/UYK-8 was a UNIVAC computer.
Development
In April 1967, UNIVAC received a contract from the U.S. Navy for design, development, testing and delivery of the AN/UYK-8 microelectronics computer for use with the AN/TYA-20.
The AN/UYK-8 was built to replace the CP-808 (Marine Corps air cooled AN/USQ-20 variant) in the Beach Relay Link-11 communication system, the AN/TYQ-3 in a AN/TYA-20
Technical
It used the same 30-bit words and instruction set as the AN/USQ-17 and AN/USQ-20 Naval Tactical Data System (NTDS) computers, built with "first generation integrated circuits". This made it about one quarter of the volume of the AN/USQ-20. It had two processors instead of just one.
Instructions were represented as 30-bit words, in the following format:
f 6 bits function code
j 3 bits jump condition designator
k 3 bits partial word designator
b 3 bits which seven index register to use (B0=non used)
s 2 bits which S (5bits) register to use S0,S1,S2,S3(P(17-13))
y 13 bits operand address in memory
memory address=Bb+Ss+y=18bit(262144Words)
Numbers were represented as full 30-bit words, this allowed for five 6-bit alphanumeric characters per word.
The main memory was increased to 262,144 words (256K words) of magnetic core memory.
The available processor registers were:
one 30-bit arithmetic (A) register.
a contiguous 30-bit Q register (total of 60 bits for the result of multiplication or the dividend in division).
seven 30-bit index (B) registers.
See also
List of UNIVAC products
History of computing hardware
References
UNIVAC hardware
Military computers
Military electronics of the United States |
https://en.wikipedia.org/wiki/Asynchronous%20serial%20communication | Asynchronous serial communication is a form of serial communication in which the communicating endpoints' interfaces are not continuously synchronized by a common clock signal. Instead of a common synchronization signal, the data stream contains synchronization information in form of start and stop signals, before and after each unit of transmission, respectively. The start signal prepares the receiver for arrival of data and the stop signal resets its state to enable triggering of a new sequence.
A common kind of start-stop transmission is ASCII over RS-232, for example for use in teletypewriter operation.
Origin
Mechanical teleprinters using 5-bit codes (see Baudot code) typically used a stop period of 1.5 bit times. Very early electromechanical teletypewriters (pre-1930) could require 2 stop bits to allow mechanical impression without buffering. Hardware which does not support fractional stop bits can communicate with a device that uses 1.5 bit times if it is configured to send 2 stop bits when transmitting and requiring 1 stop bit when receiving.
The format is derived directly from the design of the teletypewriter, which was designed this way because the electromechanical technology of its day was not precise enough for synchronous operation: thus the systems needed to be re-synchronized at the start of each character. Having been re-synchronized, the technology of the day was good enough to preserve bit-sync for the remainder of the character. The stop bits gave the system time to recover before the next start bit. Early teleprinter systems used five data bits, typically with some variant of the Baudot code.
Very early experimental printing telegraph devices used only a start bit and required manual adjustment of the receiver mechanism speed to reliably decode characters. Automatic synchronization was required to keep the transmitting and receiving units "in step". This was finally achieved by Howard Krum, who patented the start-stop method of synchronizat |
https://en.wikipedia.org/wiki/Bell%20number | In combinatorial mathematics, the Bell numbers count the possible partitions of a set. These numbers have been studied by mathematicians since the 19th century, and their roots go back to medieval Japan. In an example of Stigler's law of eponymy, they are named after Eric Temple Bell, who wrote about them in the 1930s.
The Bell numbers are denoted , where is an integer greater than or equal to zero. Starting with , the first few Bell numbers are
1, 1, 2, 5, 15, 52, 203, 877, 4140, ... .
The Bell number counts the number of different ways to partition a set that has exactly elements, or equivalently, the number of equivalence relations on it. also counts the number of different rhyme schemes for -line poems.
As well as appearing in counting problems, these numbers have a different interpretation, as moments of probability distributions. In particular, is the -th moment of a Poisson distribution with mean 1.
Counting
Set partitions
In general, is the number of partitions of a set of size . A partition of a set is defined as a family of nonempty, pairwise disjoint subsets of whose union is . For example, because the 3-element set can be partitioned in 5 distinct ways:
As suggested by the set notation above, the ordering of subsets within the family is not considered; ordered partitions are counted by a different sequence of numbers, the ordered Bell numbers. is 1 because there is exactly one partition of the empty set. This partition is itself the empty set; it can be interpreted as a family of subsets of the empty set, consisting of zero subsets. It is vacuously true that all of the subsets in this family are non-empty subsets of the empty set and that they are pairwise disjoint subsets of the empty set, because there are no subsets to have these unlikely properties.
The partitions of a set correspond one-to-one with its equivalence relations. These are binary relations that are reflexive, symmetric, and transitive. The equivalence relation correspon |
https://en.wikipedia.org/wiki/Set%20%28abstract%20data%20type%29 | In computer science, a set is an abstract data type that can store unique values, without any particular order. It is a computer implementation of the mathematical concept of a finite set. Unlike most other collection types, rather than retrieving a specific element from a set, one typically tests a value for membership in a set.
Some set data structures are designed for static or frozen sets that do not change after they are constructed. Static sets allow only query operations on their elements — such as checking whether a given value is in the set, or enumerating the values in some arbitrary order. Other variants, called dynamic or mutable sets, allow also the insertion and deletion of elements from the set.
A multiset is a special kind of set in which an element can appear multiple times in the set.
Type theory
In type theory, sets are generally identified with their indicator function (characteristic function): accordingly, a set of values of type may be denoted by or . (Subtypes and subsets may be modeled by refinement types, and quotient sets may be replaced by setoids.) The characteristic function of a set is defined as:
In theory, many other abstract data structures can be viewed as set structures with additional operations and/or additional axioms imposed on the standard operations. For example, an abstract heap can be viewed as a set structure with a min(S) operation that returns the element of smallest value.
Operations
Core set-theoretical operations
One may define the operations of the algebra of sets:
union(S,T): returns the union of sets S and T.
intersection(S,T): returns the intersection of sets S and T.
difference(S,T): returns the difference of sets S and T.
subset(S,T): a predicate that tests whether the set S is a subset of set T.
Static sets
Typical operations that may be provided by a static set structure S are:
is_element_of(x,S): checks whether the value x is in the set S.
is_empty(S): checks whether the set S is empty.
|
https://en.wikipedia.org/wiki/Divide-and-conquer%20algorithm | In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.
The divide-and-conquer technique is the basis of efficient algorithms for many problems, such as sorting (e.g., quicksort, merge sort), multiplying large numbers (e.g., the Karatsuba algorithm), finding the closest pair of points, syntactic analysis (e.g., top-down parsers), and computing the discrete Fourier transform (FFT).
Designing efficient divide-and-conquer algorithms can be difficult. As in mathematical induction, it is often necessary to generalize the problem to make it amenable to a recursive solution. The correctness of a divide-and-conquer algorithm is usually proved by mathematical induction, and its computational cost is often determined by solving recurrence relations.
Divide and conquer
The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Problems of sufficient simplicity are solved directly.
For example, to sort a given list of n natural numbers, split it into two lists of about n/2 numbers each, sort each of them in turn, and interleave both results appropriately to obtain the sorted version of the given list (see the picture). This approach is known as the merge sort algorithm.
The name "divide and conquer" is sometimes applied to algorithms that reduce each problem to only one sub-problem, such as the binary search algorithm for finding a record in a sorted list (or its analogue in numerical computing, the bisection algorithm for root finding). These algorithms can be implemented mo |
https://en.wikipedia.org/wiki/Afrotropical%20realm | The Afrotropical realm is one of Earth's eight biogeographic realms. It includes Sub-Saharan Africa, the southern Arabian Peninsula, the island of Madagascar, and the islands of the western Indian Ocean. It was formerly known as the Ethiopian Zone or Ethiopian Region.
Major ecological regions
Most of the Afrotropical realm, with the exception of Africa's southern tip, has a tropical climate. A broad belt of deserts, including the Atlantic and Sahara deserts of northern Africa and the Arabian Desert of the Arabian Peninsula, separate the Afrotropic from the Palearctic realm, which includes northern Africa and temperate Eurasia.
Sahel and Sudan
South of the Sahara, two belts of tropical grassland and savanna run east and west across the continent, from the Atlantic Ocean to the Ethiopian Highlands. Immediately south of the Sahara lies the Sahel belt, a transitional zone of semi-arid short grassland and vachellia savanna. Rainfall increases further south in the Sudanian Savanna, also known simply as the Sudan, a belt of taller grasslands and savannas. The Sudanian Savanna is home to two great flooded grasslands, the Sudd wetland in South Sudan, and the Niger Inland Delta in Mali. The forest-savanna mosaic is a transitional zone between the grasslands and the belt of tropical moist broadleaf forests near the equator.
Southern Arabian woodlands
South Arabia, which includes Yemen and parts of western Oman and southwestern Saudi Arabia, has few permanent forests. Some of the notable ones are Jabal Bura, Jabal Raymah, and Jabal Badaj in the Yemeni highland escarpment and the seasonal forests in eastern Yemen and the Dhofar region of Oman. Other woodlands scatter the land and are very small and are predominantly Juniperus or Vachellia forests.
Forest zone
The forest zone, a belt of lowland tropical moist broadleaf forests, runs across most of equatorial Africa's intertropical convergence zone. The Upper Guinean forests of West Africa extend along the coast from Guin |
https://en.wikipedia.org/wiki/Indomalayan%20realm | The Indomalayan realm is one of the eight biogeographic realms. It extends across most of South and Southeast Asia and into the southern parts of East Asia.
Also called the Oriental realm by biogeographers, Indomalaya spreads all over the Indian subcontinent and Southeast Asia to lowland southern China, and through Indonesia as far as Sumatra, Java, Bali, and Borneo, east of which lies the Wallace line, the realm boundary named after Alfred Russel Wallace which separates Indomalaya from Australasia. Indomalaya also includes the Philippines, lowland Taiwan, and Japan's Ryukyu Islands.
Most of Indomalaya was originally covered by forest, and includes tropical and subtropical moist broadleaf forests, with tropical and subtropical dry broadleaf forests predominant in much of India and parts of Southeast Asia. The tropical forests of Indomalaya are highly variable and diverse, with economically important trees, especially in the families Dipterocarpaceae and Fabaceae.
Major ecological regions
The World Wildlife Fund (WWF) divides Indomalayan realm into three bio-regions, which it defines as "geographic clusters of eco-regions that may span several habitat types, but have strong biogeographic affinities, particularly at taxonomic levels higher than the species level (genus, family)".
Indian subcontinent
The Indian subcontinent bioregion covers most of India, Bangladesh, Nepal, Bhutan, and Sri Lanka and eastern parts of Pakistan. The Hindu Kush, Karakoram, Himalaya, and Patkai ranges bound the bioregion on the northwest, north, and northeast; these ranges were formed by the collision of the northward-drifting Indian subcontinent with Asia beginning 45 million years ago. The Hindu Kush, Karakoram, and Himalaya are a major biogeographic boundary between the subtropical and tropical flora and fauna of the Indian subcontinent and the temperate-climate Palearctic realm.
Indochina
The Indochina bioregion includes most of mainland Southeast Asia, including Myanmar, Thail |
https://en.wikipedia.org/wiki/Ungermann-Bass | Ungermann-Bass, also known as UB and UB Networks, was a computer networking company in the 1980s to 1990s. Located in Santa Clara, California, UB was the first large networking company independent of any computer manufacturer. Along with competitors 3Com and Sytek, UB was responsible for starting the networking business in Silicon Valley in 1979. UB was founded by Ralph Ungermann and Charlie Bass. John Davidson, vice president of engineering, was one of the creators of NCP, the transport protocol of the ARPANET before TCP.
UB specialized in large enterprise networks connecting computer systems and devices from multiple vendors, which was unusual in the 1980s. At that time most network equipment came from computer manufacturers and usually used only protocols compatible with that one manufacturer's computer systems, such as IBM's SNA or DEC's DECnet. Many UB products initially used the XNS protocol suite, including the flagship Net/One, and later transitioned to TCP/IP as it became an industry standard in the late 1980s.
Before it became the industry standard, the Internet protocol suite TCP/IP was initially a "check box" item needed to qualify on prospective enterprise sales. As a network technology supplier to both Apple Inc. and Microsoft, in 1987-88 UB helped Apple implement their initial MacTCP offering and also helped Microsoft with a Winsock compatible software/hardware bundle for the Microsoft Windows platform. With the success of these offerings and of the Internet protocol TCP/IP, both Apple and Microsoft subsequently brought the Internet technology in-house and integrated it into their core products.
UB marketed a broadband (in the original technical sense) version of Ethernet known as 10BROAD36 in the mid 1980s. It was generally seen as hard to install. UB was one of the first network manufacturers to sell equipment that implemented Ethernet over twisted pair wiring. UB's AccessOne product line initially used the pre-standard StarLAN and, when it bec |
https://en.wikipedia.org/wiki/Tandem%20Computers | Tandem Computers, Inc. was the dominant manufacturer of fault-tolerant computer systems for ATM networks, banks, stock exchanges, telephone switching centers, 911 systems, and other similar commercial transaction processing applications requiring maximum uptime and zero data loss. The company was founded by Jimmy Treybig in 1974 in Cupertino, California. It remained independent until 1997, when it became a server division within Compaq. It is now a server division within Hewlett Packard Enterprise, following Hewlett-Packard's acquisition of Compaq and the split of Hewlett-Packard into HP Inc. and Hewlett Packard Enterprise.
Tandem's NonStop systems use a number of independent identical processors and redundant storage devices and controllers to provide automatic high-speed "failover" in the case of a hardware or software failure. To contain the scope of failures and of corrupted data, these multi-computer systems have no shared central components, not even main memory. Conventional multi-computer systems all use shared memories and work directly on shared data objects. Instead, NonStop processors cooperate by exchanging messages across a reliable fabric, and software takes periodic snapshots for possible rollback of program memory state.
Besides handling failures well, this "shared-nothing" messaging system design also scales extremely well to the largest commercial workloads. Each doubling of the total number of processors would double system throughput, up to the maximum configuration of 4000 processors. In contrast, the performance of conventional multiprocessor systems is limited by the speed of some shared memory, bus, or switch. Adding more than 4–8 processors in that manner gives no further system speedup. NonStop systems have more often been bought to meet scaling requirements than for extreme fault tolerance. They compete well against IBM's largest mainframes, despite being built from simpler minicomputer technology.
Founding
Tandem Computers was founde |
https://en.wikipedia.org/wiki/Squaring%20the%20circle | Squaring the circle is a problem in geometry first proposed in Greek mathematics. It is the challenge of constructing a square with the area of a given circle by using only a finite number of steps with a compass and straightedge. The difficulty of the problem raised the question of whether specified axioms of Euclidean geometry concerning the existence of lines and circles implied the existence of such a square.
In 1882, the task was proven to be impossible, as a consequence of the Lindemann–Weierstrass theorem, which proves that pi () is a transcendental number.
That is, is not the root of any polynomial with rational coefficients. It had been known for decades that the construction would be impossible if were transcendental, but that fact was not proven until 1882. Approximate constructions with any given non-perfect accuracy exist, and many such constructions have been found.
Despite the proof that it is impossible, attempts to square the circle have been common in pseudomathematics (i.e. the work of mathematical cranks). The expression "squaring the circle" is sometimes used as a metaphor for trying to do the impossible.
The term quadrature of the circle is sometimes used as a synonym for squaring the circle. It may also refer to approximate or numerical methods for finding the area of a circle. In general, quadrature or squaring may also be applied to other plane figures.
History
Methods to calculate the approximate area of a given circle, which can be thought of as a precursor problem to squaring the circle, were known already in many ancient cultures. These methods can be summarized by stating the approximation to that they produce. In around 2000 BCE, the Babylonian mathematicians used the approximation and at approximately the same time the ancient Egyptian mathematicians used Over 1000 years later, the Old Testament Books of Kings used the simpler approximation Ancient Indian mathematics, as recorded in the Shatapatha Brahmana and Shulba Sutras |
https://en.wikipedia.org/wiki/Adam%20Osborne | Adam Osborne (March 6, 1939 – March 18, 2003) was a British American author, software publisher, and computer designer who founded several companies in the United States and elsewhere. He introduced the Osborne 1, the first commercially successful portable computer.
Early life
Osborne was born to British parents in Bangkok, Thailand on March 6, 1939. His father, Arthur Osborne, was a teacher of eastern religion and philosophy and a lecturer in English at Chulalongkorn University. All members of the family were fluent in the Tamil language. He spent World War II in Tiruvannamalai,southern India, with his mother. There they were frequent visitors to Sri Ramana Maharshi's Ashram He attended Presentation Convent School in Kodaikanal until Class 6. In 1950, the Osborne family moved back to England. From the age of 11, he was educated at a Catholic boarding school in Warwickshire but from 1954 to 1957 was a pupil at the grammar school Leamington College for Boys, where he played chess. He graduated with a degree in chemical engineering from the University of Birmingham in 1961, and received his PhD from the University of Delaware in 1968. It was while living in the United States that he learned to write computer code. He obtained a position as a chemical engineer with Shell Oil, in California, but was dismissed.
Publishing
Osborne was a pioneer in the computer book field, founding a company in 1972 that specialized in easy-to-read computer manuals. By 1977, Osborne & Associates had 40 titles in its catalogue. In 1979, it was bought by McGraw-Hill and continued as an imprint of McGraw-Hill, "McGraw-Hill/Osborne". He also wrote several books. One of them, An Introduction To Microcomputers, sold 300,000 copies.
Computers
Osborne was known to frequent the Homebrew Computer Club's meetings around 1975. He created the first commercially available portable computer, the Osborne 1, released in April 1981. It weighed 24.5 pounds (12 kg), cost US$1795—just over half the cos |
https://en.wikipedia.org/wiki/UNIVAC%201100/2200%20series | The UNIVAC 1100/2200 series is a series of compatible 36-bit computer systems, beginning with the UNIVAC 1107 in 1962, initially made by Sperry Rand. The series continues to be supported today by Unisys Corporation as the ClearPath Dorado Series. The solid-state 1107 model number was in the same sequence as the earlier vacuum-tube computers, but the early computers were not compatible with the solid-state successors.
Architecture
Data formats
Fixed-point, either integer or fraction
Whole word – 36-bit (ones' complement)
Half word – two 18-bit fields per word (unsigned or ones' complement)
Third word – three 12-bit fields per word (ones' complement)
Quarter word – four 9-bit fields per word (unsigned)
Sixth word – six 6-bit fields per word (unsigned)
Floating point
Single precision – 36 bits: sign bit, 8-bit characteristic, 27-bit mantissa
Double precision – 72 bits: sign bit, 11-bit characteristic, 60-bit mantissa
Alphanumeric
FIELDATA – UNIVAC 6-bit code variant (no lower case characters) six characters in each 36-bit word. (FIELDATA was originally a seven-bit code of which only 64 code positions (occupying six bits) were formally defined.)
ASCII – 9 bits per character (right-most eight used for an ASCII character) four characters in each 36-bit word
Instruction format
Instructions are 36 bits long with the following fields:
f (6 bits) - function designator (opcode),
j (4 bits) - partial word designator, J-register designator, or minor function designator,
a (4 bits) - register (A, X, or R) designator or I/O designator,
x (4 bits) - index register (X) designator,
h (1 bit ) - index register increment designator,
i (1 bit) - indirect address designator,
u (16 bits) - address or operand designator.
Registers
The 128 registers of the high-speed "general register stack" ("integrated circuit registers" on the UNIVAC 1108 and UNIVAC 1106 models), map to the current data space in main storage starting at memory address zero. These registers include both user and exec |
https://en.wikipedia.org/wiki/Rechargeable%20battery | A rechargeable battery, storage battery, or secondary cell (formally a type of energy accumulator), is a type of electrical battery which can be charged, discharged into a load, and recharged many times, as opposed to a disposable or primary battery, which is supplied fully charged and discarded after use. It is composed of one or more electrochemical cells. The term "accumulator" is used as it accumulates and stores energy through a reversible electrochemical reaction. Rechargeable batteries are produced in many different shapes and sizes, ranging from button cells to megawatt systems connected to stabilize an electrical distribution network. Several different combinations of electrode materials and electrolytes are used, including lead–acid, zinc–air, nickel–cadmium (NiCd), nickel–metal hydride (NiMH), lithium-ion (Li-ion), lithium iron phosphate (LiFePO4), and lithium-ion polymer (Li-ion polymer).
Rechargeable batteries typically initially cost more than disposable batteries but have a much lower total cost of ownership and environmental impact, as they can be recharged inexpensively many times before they need replacing. Some rechargeable battery types are available in the same sizes and voltages as disposable types, and can be used interchangeably with them. Billions of dollars in research are being invested around the world for improving batteries and industry also focuses on building better batteries. Some characteristics of rechargeable battery are given below:
In rechargeable batteries, energy is induced by applying an external source to the chemical substances.
The chemical reaction that occurs in them is reversible.
Internal resistance is comparatively low.
They have a high self-discharge rate comparatively.
They have a bulky and complex design.
They have high resell value.
Applications
Devices which use rechargeable batteries include automobile starters, portable consumer devices, light vehicles (such as motorized wheelchairs, golf carts, e |
https://en.wikipedia.org/wiki/Gradient%20descent | In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent.
It is particularly useful in machine learning for minimizing the cost or loss function. Gradient descent should not be confused with local search algorithms, although both are iterative methods for optimization.
Gradient descent is generally attributed to Augustin-Louis Cauchy, who first suggested it in 1847. Jacques Hadamard independently proposed a similar method in 1907. Its convergence properties for non-linear optimization problems were first studied by Haskell Curry in 1944, with the method becoming increasingly well-studied and used in the following decades.
A simple extension of gradient descent, stochastic gradient descent, serves as the most basic algorithm used for training most deep networks today.
Description
Gradient descent is based on the observation that if the multi-variable function is defined and differentiable in a neighborhood of a point , then decreases fastest if one goes from in the direction of the negative gradient of at . It follows that, if
for a small enough step size or learning rate , then . In other words, the term is subtracted from because we want to move against the gradient, toward the local minimum. With this observation in mind, one starts with a guess for a local minimum of , and considers the sequence such that
We have a monotonic sequence
so, hopefully, the sequence converges to the desired local minimum. Note that the value of the step size is allowed to change at every iteration.
It is possible |
https://en.wikipedia.org/wiki/Xvid | Xvid (formerly "XviD") is a video codec library following the MPEG-4 video coding standard, specifically MPEG-4 Part 2 Advanced Simple Profile (ASP). It uses ASP features such as b-frames, global and quarter pixel motion compensation, lumi masking, trellis quantization, and H.263, MPEG and custom quantization matrices.
Xvid is a primary competitor of the DivX Pro Codec. In contrast with the DivX codec, which is proprietary software developed by DivX, Inc., Xvid is free software distributed under the terms of the GNU General Public License. This also means that unlike the DivX codec, which is only available for a limited number of platforms, Xvid can be used on all platforms and operating systems for which the source code can be compiled.
History
In January 2001, DivXNetworks founded OpenDivX as part of Project Mayo which was intended to be a home for open source multimedia projects. OpenDivX was an open-source MPEG-4 video codec based on a stripped-down version of the MoMuSys reference MPEG-4 encoder. The source code, however, was placed under a restrictive license and only members of the DivX Advanced Research Centre (DARC) had write access to the project's CVS. In early 2001, DARC member Sparky wrote an improved version of the encoding core called encore2. This was updated several times before, in April, it was removed from CVS without warning. The explanation given by Sparky was "We (our bosses) decided that we are not ready to have it in public yet."
In July 2001, developers started complaining about a lack of activity in the project; the last CVS commit was several months old, bugfixes were being ignored, and promised documentation had not been written. Soon after, DARC released a beta version of their closed-source commercial DivX 4 codec, which was based on encore2, saying that "what the community really wants is a Winamp, not a Linux." It was after this that a fork of OpenDivX was created, using the latest version of encore2 that was downloaded before it |
https://en.wikipedia.org/wiki/Sampling%20%28signal%20processing%29 | In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples".
A sample is a value of the signal at a point in time and/or space; this definition differs from the term's usage in statistics, which refers to a set of such values.
A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points.
The original signal can be reconstructed from a sequence of samples, up to the Nyquist limit, by passing the sequence of samples through a type of low-pass filter called a reconstruction filter.
Theory
Functions of space, time, or any other dimension can be sampled, and similarly in two or more dimensions.
For functions that vary with time, let S(t) be a continuous function (or "signal") to be sampled, and let sampling be performed by measuring the value of the continuous function every T seconds, which is called the sampling interval or sampling period. Then the sampled function is given by the sequence:
S(nT), for integer values of n.
The sampling frequency or sampling rate, fs, is the number of samples divided by the interval length over in which occur, thus , with the unit sample per second, sometimes referred to as hertz, for example e.g. 48 kHz is 48,000 samples per second.
Reconstructing a continuous function from samples is done by interpolation algorithms. The Whittaker–Shannon interpolation formula is mathematically equivalent to an ideal low-pass filter whose input is a sequence of Dirac delta functions that are modulated (multiplied) by the sample values. When the time interval between adjacent samples is a constant (T), the sequence of delta functions is called a Dirac comb. Mathematically, the modulated Dirac comb is equivalent to the product of the comb function with s(t). That math |
https://en.wikipedia.org/wiki/Orthogonalization | In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace. Formally, starting with a linearly independent set of vectors {v1, ... , vk} in an inner product space (most commonly the Euclidean space Rn), orthogonalization results in a set of orthogonal vectors {u1, ... , uk} that generate the same subspace as the vectors v1, ... , vk. Every vector in the new set is orthogonal to every other vector in the new set; and the new set and the old set have the same linear span.
In addition, if we want the resulting vectors to all be unit vectors, then we normalize each vector and the procedure is called orthonormalization.
Orthogonalization is also possible with respect to any symmetric bilinear form (not necessarily an inner product, not necessarily over real numbers), but standard algorithms may encounter division by zero in this more general setting.
Orthogonalization algorithms
Methods for performing orthogonalization include:
Gram–Schmidt process, which uses projection
Householder transformation, which uses reflection
Givens rotation
Symmetric orthogonalization, which uses the Singular value decomposition
When performing orthogonalization on a computer, the Householder transformation is usually preferred over the Gram–Schmidt process since it is more numerically stable, i.e. rounding errors tend to have less serious effects.
On the other hand, the Gram–Schmidt process produces the jth orthogonalized vector after the jth iteration, while orthogonalization using Householder reflections produces all the vectors only at the end. This makes only the Gram–Schmidt process applicable for iterative methods like the Arnoldi iteration.
The Givens rotation is more easily parallelized than Householder transformations.
Symmetric orthogonalization was formulated by Per-Olov Löwdin.
Local orthogonalization
To compensate for the loss of useful signal in traditional noise attenuation approaches because of incorrec |
https://en.wikipedia.org/wiki/Principle%20of%20maximum%20entropy | The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).
Another way of stating this: Take precisely stated prior data or testable information about a probability distribution function. Consider the set of all trial probability distributions that would encode the prior data. According to this principle, the distribution with maximal information entropy is the best choice.
History
The principle was first expounded by E. T. Jaynes in two papers in 1957 where he emphasized a natural correspondence between statistical mechanics and information theory. In particular, Jaynes offered a new and very general rationale why the Gibbsian method of statistical mechanics works. He argued that the entropy of statistical mechanics and the information entropy of information theory are basically the same thing. Consequently, statistical mechanics should be seen just as a particular application of a general tool of logical inference and information theory.
Overview
In most practical cases, the stated prior data or testable information is given by a set of conserved quantities (average values of some moment functions), associated with the probability distribution in question. This is the way the maximum entropy principle is most often used in statistical thermodynamics. Another possibility is to prescribe some symmetries of the probability distribution. The equivalence between conserved quantities and corresponding symmetry groups implies a similar equivalence for these two ways of specifying the testable information in the maximum entropy method.
The maximum entropy principle is also needed to guarantee the uniqueness and consistency of probability assignments obtained by different methods, statistical mechanics and logical inference in particular.
The maxim |
https://en.wikipedia.org/wiki/Bus%20mastering | In computing, bus mastering is a feature supported by many bus architectures that enables a device connected to the bus to initiate direct memory access (DMA) transactions. It is also referred to as first-party DMA, in contrast with third-party DMA where a system DMA controller actually does the transfer.
Some types of buses allow only one device (typically the CPU, or its proxy) to initiate transactions. Most modern bus architectures, such as PCI, allow multiple devices to bus master because it significantly improves performance for general-purpose operating systems. Some real-time operating systems prohibit peripherals from becoming bus masters, because the scheduler can no longer arbitrate for the bus and hence cannot provide deterministic latency.
While bus mastering theoretically allows one peripheral device to directly communicate with another, in practice almost all peripherals master the bus exclusively to perform DMA to main memory.
If multiple devices are able to master the bus, there needs to be a bus arbitration scheme to prevent multiple devices attempting to drive the bus simultaneously. A number of different schemes are used for this; for example SCSI has a fixed priority for each SCSI ID. PCI does not specify the algorithm to use, leaving it up to the implementation to set priorities.
See also
Master/slave (technology)
SCSI initiator and target
References
How Bus Mastering Works - Tweak3D
What is bus mastering?- Brevard User's Group
Computer buses
Motherboard |
https://en.wikipedia.org/wiki/Process%20gain | In a spread-spectrum system, the process gain (or "processing gain") is the ratio of the spread (or RF) bandwidth to the unspread (or baseband) bandwidth. It is usually expressed in decibels (dB).
For example, if a 1 kHz signal is spread to 100 kHz, the process gain expressed as a numerical ratio would be / = 100. Or in decibels, 10 log10(100) = 20 dB.
Note that process gain does not reduce the effects of wideband thermal noise. It can be shown that a direct-sequence spread-spectrum (DSSS) system has exactly the same bit error behavior as a non-spread-spectrum system with the same modulation format. Thus, on an additive white Gaussian noise (AWGN) channel without interference, a spread system requires the same transmitter power as an unspread system, all other things being equal.
Unlike a conventional communication system, however, a DSSS system does have a certain resistance against narrowband interference, as the interference is not subject to the process gain of the DSSS signal, and hence the signal-to-interference ratio is improved.
In frequency modulation (FM), the processing gain can be expressed as
where:
Gp is the processing gain,
Bn is the noise bandwidth,
Δf is the peak frequency deviation,
W is the sinusoidal modulating frequency.
Signal processing |
https://en.wikipedia.org/wiki/Additive%20white%20Gaussian%20noise | Additive white Gaussian noise (AWGN) is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. The modifiers denote specific characteristics:
Additive because it is added to any noise that might be intrinsic to the information system.
White refers to the idea that it has uniform power spectral density across the frequency band for the information system. It is an analogy to the color white which may be realized by uniform emissions at all frequencies in the visible spectrum.
Gaussian because it has a normal distribution in the time domain with an average time domain value of zero (Gaussian process).
Wideband noise comes from many natural noise sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson–Nyquist noise), shot noise, black-body radiation from the earth and other warm objects, and from celestial sources such as the Sun. The central limit theorem of probability theory indicates that the summation of many random processes will tend to have distribution called Gaussian or Normal.
AWGN is often used as a channel model in which the only impairment to communication is a linear addition of wideband or white noise with a constant spectral density (expressed as watts per hertz of bandwidth) and a Gaussian distribution of amplitude. The model does not account for fading, frequency selectivity, interference, nonlinearity or dispersion. However, it produces simple and tractable mathematical models which are useful for gaining insight into the underlying behavior of a system before these other phenomena are considered.
The AWGN channel is a good model for many satellite and deep space communication links. It is not a good model for most terrestrial links because of multipath, terrain blocking, interference, etc. However, for terrestrial path modeling, AWGN is commonly used to simulate background noise of the channel under study, in addition to multipath, ter |
https://en.wikipedia.org/wiki/MATH-MATIC | MATH-MATIC is the marketing name for the AT-3 (Algebraic Translator 3) compiler, an early programming language for the UNIVAC I and UNIVAC II.
MATH-MATIC was written beginning around 1955 by a team led by Charles Katz under the direction of Grace Hopper. A preliminary manual was produced in 1957 and a final manual the following year.
Syntactically, MATH-MATIC was similar to Univac's contemporaneous business-oriented language, FLOW-MATIC, differing in providing algebraic-style expressions and floating-point arithmetic, and arrays rather than record structures.
Notable features
Expressions in MATH-MATIC could contain numeric exponents, including decimals and fractions, by way of a custom typewriter.
MATH-MATIC programs could include inline assembler sections of ARITH-MATIC code and UNIVAC machine code.
The UNIVAC I had only 1000 words of memory, and the successor UNIVAC II as little as 2000. MATH-MATIC allowed for larger programs, automatically generating code to read overlay segments from UNISERVO tape as required. The compiler attempted to avoid splitting loops across segments.
Influence
In proposing the collaboration with the ACM that led to ALGOL 58, the Gesellschaft für Angewandte Mathematik und Mechanik wrote that it considered MATH-MATIC the closest available language to its own proposal.
In contrast to Backus' FORTRAN, MATH-MATIC did not emphasise execution speed of compiled programs. The UNIVAC machines did not have floating-point hardware, and MATH-MATIC was translated via A-3 (ARITH-MATIC) pseudo-assembler code rather than directly to UNIVAC machine code, limiting its usefulness.
MATH-MATIC Sample program
A sample MATH-MATIC program:
Notes
References
Numerical programming languages
Programming languages
Programming languages created in 1957 |
https://en.wikipedia.org/wiki/MOS%20Technology%206581 | The MOS Technology 6581/8580 SID (Sound Interface Device) is the built-in programmable sound generator chip of the Commodore CBM-II, Commodore 64, Commodore 128, and MAX Machine home computers.
Together with the VIC-II graphics chip, the SID was instrumental in making the C64 the best-selling home computer in history, and is partly credited for initiating the demoscene.
History
The SID was devised by engineer Bob Yannes, who later co-founded the Ensoniq digital synthesizer and sampler company. Yannes headed a team that included himself, two technicians and a CAD operator, who designed and completed the chip in five months in the latter half of 1981. Yannes was inspired by previous work in the synthesizer industry and was not impressed by the current state of computer sound chips. Instead, he wanted a high-quality instrument chip, which is the reason why the SID has features like the envelope generator, previously not found in home computer sound chips.
Emphasis during chip design was on high-precision frequency control, and the SID was originally designed to have 32 independent voices, sharing a common wavetable lookup scheme that would be time multiplexed. However, these features could not be finished in time, so instead the mask work for a certain working oscillator was simply replicated three times across the chip's surface, creating three voices each with its own oscillator. Another feature that was not incorporated in the final design was a frequency look-up table for the most common musical notes, a feature that was dropped because of space limitations. The support for an audio input pin was a feature Yannes added without asking, which in theory would have allowed the chip to be used as a simple effect processor. The masks were produced in 7-micrometer technology to gain a high yield; the state of the art at the time was 6-micrometer technologies.
The chip, like the first product using it (the Commodore 64), was finished in time for the Consumer Electron |
https://en.wikipedia.org/wiki/Ionizing%20radiation | Ionizing radiation (or ionising radiation), including nuclear radiation, consists of subatomic particles or electromagnetic waves that have sufficient energy to ionize atoms or molecules by detaching electrons from them. Some particles can travel up to 99% of the speed of light, and the electromagnetic waves are on the high-energy portion of the electromagnetic spectrum.
Gamma rays, X-rays, and the higher energy ultraviolet part of the electromagnetic spectrum are ionizing radiation, whereas the lower energy ultraviolet, visible light, nearly all types of laser light, infrared, microwaves, and radio waves are non-ionizing radiation. The boundary between ionizing and non-ionizing radiation in the ultraviolet area cannot be sharply defined, as different molecules and atoms ionize at different energies. The energy of ionizing radiation starts between 10 electronvolts (eV) and 33 eV.
Typical ionizing subatomic particles include alpha particles, beta particles, and neutrons. These are typically created by radioactive decay, and almost all are energetic enough to ionize. There are also secondary cosmic particles produced after cosmic rays interact with Earth's atmosphere, including muons, mesons, and positrons. Cosmic rays may also produce radioisotopes on Earth (for example, carbon-14), which in turn decay and emit ionizing radiation. Cosmic rays and the decay of radioactive isotopes are the primary sources of natural ionizing radiation on Earth, contributing to background radiation. Ionizing radiation is also generated artificially by X-ray tubes, particle accelerators, and nuclear fission.
Ionizing radiation is not immediately detectable by human senses, so instruments such as Geiger counters are used to detect and measure it. However, very high energy particles can produce visible effects on both organic and inorganic matter (e.g. water lighting in Cherenkov radiation) or humans (e.g. acute radiation syndrome).
Ionizing radiation is used in a wide variety of field |
https://en.wikipedia.org/wiki/Tim%20Paterson | Tim Paterson (born 1 June 1956) is an American computer programmer, best known for creating 86-DOS, an operating system for the Intel 8086. This system emulated the application programming interface (API) of CP/M, which was created by Gary Kildall. 86-DOS later formed the basis of MS-DOS, the most widely used personal computer operating system in the 1980s.
Biography
Paterson was educated in the Seattle Public Schools, graduating from Ingraham High School in 1974. He attended the University of Washington, working as a repair technician for The Retail Computer Store in the Green Lake area of Seattle, Washington, and graduated magna cum laude with a degree in Computer Science in June 1978. He went to work for Seattle Computer Products as a designer and engineer. He designed the hardware of Microsoft's Z-80 SoftCard which had a Z80 CPU and ran the CP/M operating system on an Apple II.
A month later, Intel released the 8086 CPU, and Paterson went to work designing an S-100 8086 board, which went to market in November 1979. The only commercial software that existed for the board was Microsoft's Standalone Disk BASIC-86. The standard CP/M operating system at the time was not available for this CPU and without a true operating system, sales were slow. Paterson began work on QDOS (Quick and Dirty Operating System) in April 1980 to fill that void, copying the APIs of CP/M from references, including the published CP/M manual, so that it would be highly compatible. QDOS was soon renamed as 86-DOS. Version 0.10 was complete by July 1980. By version 1.14, 86-DOS had grown to lines of assembly code. In December 1980, Microsoft secured the rights to market 86-DOS to other hardware manufacturers.
While acknowledging that he made 86-DOS compatible with CP/M, Paterson has maintained that the 86-DOS program was his original work and has denied allegations that he referred to CP/M code while writing it. When a book appeared in 2004 claiming that 86-DOS was an unoriginal "rip-off" o |
https://en.wikipedia.org/wiki/Spectral%20density | The power spectrum of a time series describes the distribution of power into frequency components composing that signal. According to Fourier analysis, any physical signal can be decomposed into a number of discrete frequencies, or a spectrum of frequencies over a continuous range. The statistical average of any sort of signal (including noise) as analyzed in terms of its frequency content, is called its spectrum.
When the energy of the signal is concentrated around a finite time interval, especially if its total energy is finite, one may compute the energy spectral density. More commonly used is the power spectral density (or simply power spectrum), which applies to signals existing over all time, or over a time period large enough (especially in relation to the duration of a measurement) that it could as well have been over an infinite time interval. The power spectral density (PSD) then refers to the spectral energy distribution that would be found per unit time, since the total energy of such a signal over all time would generally be infinite. Summation or integration of the spectral components yields the total power (for a physical process) or variance (in a statistical process), identical to what would be obtained by integrating over the time domain, as dictated by Parseval's theorem.
The spectrum of a physical process often contains essential information about the nature of . For instance, the pitch and timbre of a musical instrument are immediately determined from a spectral analysis. The color of a light source is determined by the spectrum of the electromagnetic wave's electric field as it fluctuates at an extremely high frequency. Obtaining a spectrum from time series such as these involves the Fourier transform, and generalizations based on Fourier analysis. In many cases the time domain is not specifically employed in practice, such as when a dispersive prism is used to obtain a spectrum of light in a spectrograph, or when a sound is perceived |
https://en.wikipedia.org/wiki/Standardized%20moment | In probability theory and statistics, a standardized moment of a probability distribution is a moment (often a higher degree central moment) that is normalized, typically by a power of the standard deviation, rendering the moment scale invariant. The shape of different probability distributions can be compared using standardized moments.
Standard normalization
Let X be a random variable with a probability distribution P and mean value (i.e. the first raw moment or moment about zero), the operator E denoting the expected value of X. Then the standardized moment of degree k is that is, the ratio of the kth moment about the mean
to the kth power of the standard deviation,
The power of k is because moments scale as meaning that they are homogeneous functions of degree k, thus the standardized moment is scale invariant. This can also be understood as being because moments have dimension; in the above ratio defining standardized moments, the dimensions cancel, so they are dimensionless numbers.
The first four standardized moments can be written as:
For skewness and kurtosis, alternative definitions exist, which are based on the third and fourth cumulant respectively.
Other normalizations
Another scale invariant, dimensionless measure for characteristics of a distribution is the coefficient of variation, . However, this is not a standardized moment, firstly because it is a reciprocal, and secondly because is the first moment about zero (the mean), not the first moment about the mean (which is zero).
See Normalization (statistics) for further normalizing ratios.
See also
Coefficient of variation
Moment (mathematics)
Central moment
References
Statistical deviation and dispersion
Statistical ratios
Moment (mathematics) |
https://en.wikipedia.org/wiki/Tensor%20contraction | In multilinear algebra, a tensor contraction is an operation on a tensor that arises from the natural pairing of a finite-dimensional vector space and its dual. In components, it is expressed as a sum of products of scalar components of the tensor(s) caused by applying the summation convention to a pair of dummy indices that are bound to each other in an expression. The contraction of a single mixed tensor occurs when a pair of literal indices (one a subscript, the other a superscript) of the tensor are set equal to each other and summed over. In Einstein notation this summation is built into the notation. The result is another tensor with order reduced by 2.
Tensor contraction can be seen as a generalization of the trace.
Abstract formulation
Let V be a vector space over a field k. The core of the contraction operation, and the simplest case, is the natural pairing of V with its dual vector space V∗. The pairing is the linear transformation from the tensor product of these two spaces to the field k:
corresponding to the bilinear form
where f is in V∗ and v is in V. The map C defines the contraction operation on a tensor of type , which is an element of . Note that the result is a scalar (an element of k). Using the natural isomorphism between and the space of linear transformations from V to V, one obtains a basis-free definition of the trace.
In general, a tensor of type (with and ) is an element of the vector space
(where there are m factors V and n factors V∗). Applying the natural pairing to the kth V factor and the lth V∗ factor, and using the identity on all other factors, defines the (k, l) contraction operation, which is a linear map which yields a tensor of type . By analogy with the case, the general contraction operation is sometimes called the trace.
Contraction in index notation
In tensor index notation, the basic contraction of a vector and a dual vector is denoted by
which is shorthand for the explicit coordinate summation |
https://en.wikipedia.org/wiki/Digital%20Picture%20Exchange | Digital Picture Exchange (DPX) is a common file format for digital intermediate and visual effects work and is a SMPTE standard (ST 268-1:2014). The file format is most commonly used to represent the density of each colour channel of a scanned negative film in an uncompressed "logarithmic" image where the gamma of the original camera negative is preserved as taken by a film scanner. For this reason, DPX is the worldwide-chosen format for still frames storage in most digital intermediate post-production facilities and film labs. Other common video formats are supported as well (see below), from video to purely digital ones, making DPX a file format suitable for almost any raster digital imaging applications. DPX provides, in fact, a great deal of flexibility in storing colour information, colour spaces and colour planes for exchange between production facilities. Multiple forms of packing and alignment are possible. The DPX specification allows for a wide variety of metadata to further clarify information stored (and storable) within each file.
The DPX file format was originally derived from the Kodak Cineon open file format (.cin file extension) used for digital images generated by Kodak's original film scanner. The original DPX (version 1.0) specifications are part of SMPTE 268M-1994. The specification was later improved and published by SMPTE as ANSI/SMPTE 268M-2003. Academy Density Exchange (ADX) support for the Academy Color Encoding System are added in the current version of the standard SMPTE ST 268-1:2014. Extensions for high-dynamic-range video and wide color gamut are standardized in SMPTE ST 268-2:2018.
Metadata and standard flexibility
SMPTE specifications dictate a mild number of compulsory metadata, like image resolution, color space details (channel depth, colorimetric metric, etc.), number of planes/subimages, as well as original filename and creation date/time, creator's name, project name, copyright information, and so on.
Furthermore, a couple |
https://en.wikipedia.org/wiki/Mixed%20tensor | In tensor analysis, a mixed tensor is a tensor which is neither strictly covariant nor strictly contravariant; at least one of the indices of a mixed tensor will be a subscript (covariant) and at least one of the indices will be a superscript (contravariant).
A mixed tensor of type or valence , also written "type (M, N)", with both M > 0 and N > 0, is a tensor which has M contravariant indices and N covariant indices. Such a tensor can be defined as a linear function which maps an (M + N)-tuple of M one-forms and N vectors to a scalar.
Changing the tensor type
Consider the following octet of related tensors:
The first one is covariant, the last one contravariant, and the remaining ones mixed. Notationally, these tensors differ from each other by the covariance/contravariance of their indices. A given contravariant index of a tensor can be lowered using the metric tensor , and a given covariant index can be raised using the inverse metric tensor . Thus, could be called the index lowering operator and the index raising operator.
Generally, the covariant metric tensor, contracted with a tensor of type (M, N), yields a tensor of type (M − 1, N + 1), whereas its contravariant inverse, contracted with a tensor of type (M, N), yields a tensor of type (M + 1, N − 1).
Examples
As an example, a mixed tensor of type (1, 2) can be obtained by raising an index of a covariant tensor of type (0, 3),
where is the same tensor as , because
with Kronecker acting here like an identity matrix.
Likewise,
Raising an index of the metric tensor is equivalent to contracting it with its inverse, yielding the Kronecker delta,
so any mixed version of the metric tensor will be equal to the Kronecker delta, which will also be mixed.
See also
Covariance and contravariance of vectors
Einstein notation
Ricci calculus
Tensor (intrinsic definition)
Two-point tensor
References
External links
Index Gymnastics, Wolfram Alpha
Tensors |
https://en.wikipedia.org/wiki/Adjugate%20matrix | In linear algebra, the adjugate or classical adjoint of a square matrix is the transpose of its cofactor matrix and is denoted by . It is also occasionally known as adjunct matrix, or "adjoint", though the latter term today normally refers to a different concept, the adjoint operator which for a matrix is the conjugate transpose.
The product of a matrix with its adjugate gives a diagonal matrix (entries not on the main diagonal are zero) whose diagonal entries are the determinant of the original matrix:
where is the identity matrix of the same size as . Consequently, the multiplicative inverse of an invertible matrix can be found by dividing its adjugate by its determinant.
Definition
The adjugate of is the transpose of the cofactor matrix of ,
In more detail, suppose is a unital commutative ring and is an matrix with entries from . The -minor of , denoted , is the determinant of the matrix that results from deleting row and column of . The cofactor matrix of is the matrix whose entry is the cofactor of , which is the -minor times a sign factor:
The adjugate of is the transpose of , that is, the matrix whose entry is the cofactor of ,
Important consequence
The adjugate is defined so that the product of with its adjugate yields a diagonal matrix whose diagonal entries are the determinant . That is,
where is the identity matrix. This is a consequence of the Laplace expansion of the determinant.
The above formula implies one of the fundamental results in matrix algebra, that is invertible if and only if is an invertible element of . When this holds, the equation above yields
Examples
1 × 1 generic matrix
Since the determinant of a 0 × 0 matrix is 1, the adjugate of any 1 × 1 matrix (complex scalar) is . Observe that
2 × 2 generic matrix
The adjugate of the 2 × 2 matrix
is
By direct computation,
In this case, it is also true that ((A)) = (A) and hence that ((A)) = A.
3 × 3 generic matrix
Consider a 3 × 3 matrix
Its cofa |
https://en.wikipedia.org/wiki/Covariance%20and%20contravariance%20of%20vectors | In physics, especially in multilinear algebra and tensor analysis, covariance and contravariance describe how the quantitative description of certain geometric or physical entities changes with a change of basis. In modern mathematical notation, the role is sometimes swapped.
A simple illustrative case is that of a vector. For a vector, once a set of basis vectors has been defined, then the components of that vector will always vary opposite to that of the basis vectors. A vector is therefore a contravariant tensor. Take a standard position vector for example. By changing the scale of the reference axes from meters to centimeters (that is, dividing the scale of the reference axes by 100, so that the basis vectors now are meters long), the components of the measured position vector are multiplied by 100. A vector's components change scale inversely to changes in scale to the reference axes, and consequently a vector is called a contravariant tensor.
In contrast, a covector, also called a dual vector, has components that vary with the basis vectors in the corresponding vector space. It is an example of a covariant tensor. A covector is an object that represents a linear map from vectors to scalars. It is actually not a vector, but an object that lives in a dual vector space. Some good examples of covectors are dot product operators involving vectors. For example if is a vector, then a corresponding object in the dual space would be the linear operator . Sometimes, the components of the covector are referred to as the covariant components of , although this is potentially misleading, (due to a vector having components that always vary in the contravariant sense). Despite potential confusion, this is what will be meant when the "covariant components of a vector" are referred to herein.
The gradient is often cited as an example of a covector, but this is incorrect. If the components of the gradient of a function , , are expressed in terms of a give |
https://en.wikipedia.org/wiki/Orthographic%20projection | Orthographic projection (also orthogonal projection and analemma) is a means of representing three-dimensional objects in two dimensions. Orthographic projection is a form of parallel projection in which all the projection lines are orthogonal to the projection plane, resulting in every plane of the scene appearing in affine transformation on the viewing surface. The obverse of an orthographic projection is an oblique projection, which is a parallel projection in which the projection lines are not orthogonal to the projection plane.
The term orthographic sometimes means a technique in multiview projection in which principal axes or the planes of the subject are also parallel with the projection plane to create the primary views. If the principal planes or axes of an object in an orthographic projection are not parallel with the projection plane, the depiction is called axonometric or an auxiliary views. (Axonometric projection is synonymous with parallel projection.) Sub-types of primary views include plans, elevations, and sections; sub-types of auxiliary views include isometric, dimetric, and trimetric projections.
A lens that provides an orthographic projection is an object-space telecentric lens.
Geometry
A simple orthographic projection onto the plane z = 0 can be defined by the following matrix:
For each point v = (vx, vy, vz), the transformed point Pv would be
Often, it is more useful to use homogeneous coordinates. The transformation above can be represented for homogeneous coordinates as
For each homogeneous vector v = (vx, vy, vz, 1), the transformed vector Pv would be
In computer graphics, one of the most common matrices used for orthographic projection can be defined by a 6-tuple, (left, right, bottom, top, near, far), which defines the clipping planes. These planes form a box with the minimum corner at (left, bottom, -near) and the maximum corner at (right, top, -far).
The box is translated so that its center is at the origin, then it is |
https://en.wikipedia.org/wiki/Memory%20effect | Memory effect, also known as battery effect, lazy battery effect, or battery memory, is an effect observed in nickel-cadmium rechargeable batteries that causes them to hold less charge. It describes the situation in which nickel-cadmium batteries gradually lose their maximum energy capacity if they are repeatedly recharged after being only partially discharged. The battery appears to "remember" the smaller capacity.
True memory effect
The term "memory" came from an aerospace nickel-cadmium application in which the cells were repeatedly discharged to 25% of available capacity (give or take 1%) by exacting computer control, then recharged to 100% capacity without overcharge. This long-term, repetitive cycle régime, with no provision for overcharge, resulted in a loss of capacity beyond the 25% discharge point. True memory cannot exist if any one (or more) of the following conditions holds:
batteries achieve full overcharge.
discharge is not exactly the same each cycle, within plus or minus 3%
discharge is to less than 1.0 volt per cell
True memory-effect is specific to sintered-plate nickel-cadmium cells, and is exceedingly difficult to reproduce, especially in lower ampere-hour cells. In one particular test program designed to induce the effect, none was found after more than 700 precisely-controlled charge/discharge cycles. In the program, spirally-wound one-ampere-hour cells were used. In a follow-up program, 20-ampere-hour aerospace-type cells were used on a similar test régime; memory effects were observed after a few hundred cycles.
Other problems perceived as memory effect
Phenomena which are not true memory effects may also occur in battery types other than sintered-plate nickel-cadmium cells. In particular, lithium-based cells, not normally subject to the memory effect, may change their voltage levels so that a virtual decrease of capacity may be perceived by the battery control system.
Temporary effects
Voltage depression due to long-term over-chargi |
https://en.wikipedia.org/wiki/List%20of%20order%20structures%20in%20mathematics | In mathematics, and more specifically in order theory, several different types of ordered set have been studied.
They include:
Cyclic orders, orderings in which triples of elements are either clockwise or counterclockwise
Lattices, partial orders in which each pair of elements has a greatest lower bound and a least upper bound. Many different types of lattice have been studied; see map of lattices for a list.
Partially ordered sets (or posets), orderings in which some pairs are comparable and others might not be
Preorders, a generalization of partial orders allowing ties (represented as equivalences and distinct from incomparabilities)
Semiorders, partial orders determined by comparison of numerical values, in which values that are too close to each other are incomparable; a subfamily of partial orders with certain restrictions
Total orders, orderings that specify, for every two distinct elements, which one is less than the other
Weak orders, generalizations of total orders allowing ties (represented either as equivalences or, in strict weak orders, as transitive incomparabilities)
Well-orders, total orders in which every non-empty subset has a least element
Well-quasi-orderings, a class of preorders generalizing the well-orders
See also
Glossary of order theory
List of order theory topics
Mathematics-related lists
Order theory |
https://en.wikipedia.org/wiki/Internet%20Society | The Internet Society (ISOC) is an American nonprofit advocacy organization founded in 1992 with local chapters around the world. Its mission is "to promote the open development, evolution, and use of the Internet for the benefit of all people throughout the world." It has offices in Reston, Virginia, U.S., and Geneva, Switzerland.
Organization
The Internet Society has regional bureaus worldwide, composed of chapters, organizational members, and, as of July 2020, more than 70,000 individual members. The Internet Society has a staff of more than 100 and was governed by a board of trustees, whose members are appointed or elected by the society's chapters, organization members, and the Internet Engineering Task Force (IETF). The IETF comprised the Internet Society's volunteer base. Its leadership includes Chairman of the Board of Trustees, Ted Hardie; and President and CEO, Andrew Sullivan.
The Internet Society created the Public Interest Registry (PIR), launched the Internet Hall of Fame, and served as the organizational home of the IETF. The Internet Society Foundation was created in 2017 as its independent philanthropic arm, which awarded grants to organizations.
History
In 1991, the National Science Foundation (NSF) contract with the Corporation for National Research Initiatives (CNRI) to operate the Internet Engineering Task Force (IETF) expired. The then Internet Activities Board (IAB) sought to create a non-profit institution which could take over the role. In 1992 Vint Cerf, Bob Kahn and Lyman Chapin announced the formation of the Internet Society as "a professional society to facilitate, support, and promote the evolution and growth of the Internet as a global research communications infrastructure," which would incorporate the IAB, the IETF, and the Internet Research Task Force (IRTF), plus the organization of the annual INET meetings. This arrangement was formalized in RFC1602 in 1993.
In 1999, after Jon Postel's death, ISOC established the Jonathan B. Po |
https://en.wikipedia.org/wiki/Geometric%20Brownian%20motion | A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift. It is an important example of stochastic processes satisfying a stochastic differential equation (SDE); in particular, it is used in mathematical finance to model stock prices in the Black–Scholes model.
Technical definition: the SDE
A stochastic process St is said to follow a GBM if it satisfies the following stochastic differential equation (SDE):
where is a Wiener process or Brownian motion, and ('the percentage drift') and ('the percentage volatility') are constants.
The former parameter is used to model deterministic trends, while the latter parameter models unpredictable events occurring during the motion.
Solving the SDE
For an arbitrary initial value S0 the above SDE has the analytic solution (under Itô's interpretation):
The derivation requires the use of Itô calculus. Applying Itô's formula leads to
where is the quadratic variation of the SDE.
When , converges to 0 faster than ,
since . So the above infinitesimal can be simplified by
Plugging the value of in the above equation and simplifying we obtain
Taking the exponential and multiplying both sides by gives the solution claimed above.
Arithmetic Brownian Motion
The process for , satisfying the SDE
or more generally the process solving the SDE
where and are real constants and for an initial condition , is called an Arithmetic Brownian Motion (ABM). This was the model postulated by Louis Bachelier in 1900 for stock prices, in the first published attempt to model Brownian motion, known today as Bachelier model. As was shown above, the ABM SDE can be obtained through the logarithm of a GBM via Itô's formula. Similarly, a GBM can be obtained by exponentiation of an ABM through Itô's formula.
Properties of GBM
The above solut |
https://en.wikipedia.org/wiki/Polynomial%20interpolation | In numerical analysis, polynomial interpolation is the interpolation of a given bivariate data set by the polynomial of lowest possible degree that passes through the points of the dataset.
Given a set of data points , with no two the same, a polynomial function is said to interpolate the data if for each .
There is always a unique such polynomial, commonly given by two explicit formulas, the Lagrange polynomials and Newton polynomials.
Applications
The original use of interpolation polynomials was to approximate values of important transcendental functions such as natural logarithm and trigonometric functions. Starting with a few accurately computed data points, the corresponding interpolation polynomial will approximate the function at an arbitrary nearby point. Polynomial interpolation also forms the basis for algorithms in numerical quadrature (Simpson's rule) and numerical ordinary differential equations (multigrid methods).
In computer graphics, polynomials can be used to approximate complicated plane curves given a few specified points, for example the shapes of letters in typography. This is usually done with Bézier curves, which are a simple generalization of interpolation polynomials (having specified tangents as well as specified points).
In numerical analysis, polynomial interpolation is essential to perform sub-quadratic multiplication and squaring, such as Karatsuba multiplication and Toom–Cook multiplication, where interpolation through points on a product polynomial yields the specific product required. For example, given a = f(x) = a0x0 + a1x1 + ··· and b = g(x) = b0x0 + b1x1 + ···, the product ab is a specific value of W(x) = f(x)g(x). One may easily find points along W(x) at small values of x, and interpolation based on those points will yield the terms of W(x) and the specific product ab. As fomulated in Karatsuba multiplication, this technique is substantially faster than quadratic multiplication, even for modest-sized inputs, especi |
https://en.wikipedia.org/wiki/Euler%20line | In geometry, the Euler line, named after Leonhard Euler (), is a line determined from any triangle that is not equilateral. It is a central line of the triangle, and it passes through several important points determined from the triangle, including the orthocenter, the circumcenter, the centroid, the Exeter point and the center of the nine-point circle of the triangle.
The concept of a triangle's Euler line extends to the Euler line of other shapes, such as the quadrilateral and the tetrahedron.
Triangle centers on the Euler line
Individual centers
Euler showed in 1765 that in any triangle, the orthocenter, circumcenter and centroid are collinear. This property is also true for another triangle center, the nine-point center, although it had not been defined in Euler's time. In equilateral triangles, these four points coincide, but in any other triangle they are all distinct from each other, and the Euler line is determined by any two of them.
Other notable points that lie on the Euler line include the de Longchamps point, the Schiffler point, the Exeter point, and the Gossard perspector. However, the incenter generally does not lie on the Euler line; it is on the Euler line only for isosceles triangles, for which the Euler line coincides with the symmetry axis of the triangle and contains all triangle centers.
The tangential triangle of a reference triangle is tangent to the latter's circumcircle at the reference triangle's vertices. The circumcenter of the tangential triangle lies on the Euler line of the reference triangle. The center of similitude of the orthic and tangential triangles is also on the Euler line.
Proofs
A vector proof
Let be a triangle. A proof of the fact that the circumcenter , the centroid and the orthocenter are collinear relies on free vectors. We start by stating the prerequisites. First, satisfies the relation
This follows from the fact that the absolute barycentric coordinates of are . Further, the problem of Sylvester reads |
https://en.wikipedia.org/wiki/Liouville%E2%80%93Neumann%20series | In mathematics, the Liouville–Neumann series is an infinite series that corresponds to the resolvent formalism technique of solving the Fredholm integral equations in Fredholm theory.
Definition
The Liouville–Neumann (iterative) series is defined as
which, provided that is small enough so that the series converges, is the unique continuous solution of the Fredholm integral equation of the second kind,
If the nth iterated kernel is defined as n−1 nested integrals of n operators ,
then
with
so K0 may be taken to be .
The resolvent (or solving kernel for the integral operator) is then given by a schematic analog "geometric series",
where K0 has been taken to be .
The solution of the integral equation thus becomes simply
Similar methods may be used to solve the Volterra equations.
See also
Neumann series
References
Mathews, Jon; Walker, Robert L. (1970), Mathematical methods of physics (2nd ed.), New York: W. A. Benjamin,
Fredholm theory
Mathematical series
Mathematical physics |
https://en.wikipedia.org/wiki/Crusher | A crusher is a machine designed to reduce large rocks into smaller rocks, gravel, sand or rock dust.
Crushers may be used to reduce the size, or change the form, of waste materials so they can be more easily disposed of or recycled, or to reduce the size of a solid mix of raw materials (as in rock ore), so that pieces of different composition can be differentiated. Crushing is the process of transferring a force amplified by mechanical advantage through a material made of molecules that bond together more strongly, and resist deformation more, than those in the material being crushed do. Crushing devices hold material between two parallel or tangent solid surfaces, and apply sufficient force to bring the surfaces together to generate enough energy within the material being crushed so that its molecules separate from (fracturing), or change alignment in relation to (deformation), each other. The earliest crushers were hand-held stones, where the weight of the stone provided a boost to muscle power, used against a stone anvil. Querns and mortars are types of these crushing devices.
Background history
In industry, crushers are machines which use a metal surface to break or compress materials into small fractional chunks or denser masses. Throughout most of industrial history, the greater part of crushing and mining part of the process occurred under muscle power as the application of force concentrated in the tip of the miners pick or sledge hammer driven drill bit. Before explosives came into widespread use in bulk mining in the mid-nineteenth century, most initial ore crushing and sizing was by hand and hammers at the mine or by water powered trip hammers in the small charcoal fired smithies and iron works typical of the Renaissance through the early-to-middle industrial revolution. It was only after explosives, and later early powerful steam shovels produced large chunks of materials, chunks originally reduced by hammering in the mine before being loaded into sac |
https://en.wikipedia.org/wiki/Inverse%20problem | An inverse problem in science is the process of calculating from a set of observations the causal factors that produced them: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. It is called an inverse problem because it starts with the effects and then calculates the causes. It is the inverse of a forward problem, which starts with the causes and then calculates the effects.
Inverse problems are some of the most important mathematical problems in science and mathematics because they tell us about parameters that we cannot directly observe. They have wide application in system identification, optics, radar, acoustics, communication theory, signal processing, medical imaging, computer vision, geophysics, oceanography, astronomy, remote sensing, natural language processing, machine learning, nondestructive testing, slope stability analysis and many other fields.
History
Starting with the effects to discover the causes has concerned physicists for centuries. A historical example is the calculations of Adams and Le Verrier which led to the discovery of Neptune from the perturbed trajectory of Uranus. However, a formal study of inverse problems was not initiated until the 20th century.
One of the earliest examples of a solution to an inverse problem was discovered by Hermann Weyl and published in 1911, describing the asymptotic behavior of eigenvalues of the Laplace–Beltrami operator. Today known as Weyl's law, it is perhaps most easily understood as an answer to the question of whether it is possible to hear the shape of a drum. Weyl conjectured that the eigenfrequencies of a drum would be related to the area and perimeter of the drum by a particular equation, a result improved upon by later mathematicians.
The field of inverse problems was later touched on by Soviet-Armenian physicist, Viktor Ambartsumian.
While still a student, Ambartsum |
https://en.wikipedia.org/wiki/It%C3%B4%27s%20lemma | In mathematics, Itô's lemma or Itô's formula (also called the Itô-Doeblin formula, especially in the French literature) is an identity used in Itô calculus to find the differential of a time-dependent function of a stochastic process. It serves as the stochastic calculus counterpart of the chain rule. It can be heuristically derived by forming the Taylor series expansion of the function up to its second derivatives and retaining terms up to first order in the time increment and second order in the Wiener process increment. The lemma is widely employed in mathematical finance, and its best known application is in the derivation of the Black–Scholes equation for option values.
Kiyoshi Itô published a proof of the formula in 1951.
Motivation
Suppose we are given the stochastic differential equation
where is a Wiener process and the functions are deterministic (not stochastic) functions of time. In general, it's not possible to write a solution directly in terms of However, we can formally write an integral solution
This expression lets us easily read off the mean and variance of (which has no higher moments). First, notice that every individually has mean 0, so the expectation value of is simply the integral of the drift function:
Similarly, because the terms have variance 1 and no correlation with one another, the variance of is simply the integral of the variance of each infinitesimal step in the random walk:
However, sometimes we are faced with a stochastic differential equation for a more complex process in which the process appears on both sides of the differential equation. That is, say
for some functions and In this case, we cannot immediately write a formal solution as we did for the simpler case above. Instead, we hope to write the process as a function of a simpler process taking the form above. That is, we want to identify three functions and such that and In practice, Ito's lemma is used in order to find this transformation. Finall |
https://en.wikipedia.org/wiki/Normal%20number | In mathematics, a real number is said to be simply normal in an integer base b if its infinite sequence of digits is distributed uniformly in the sense that each of the b digit values has the same natural density 1/b. A number is said to be normal in base b if, for every positive integer n, all possible strings n digits long have density b−n.
Intuitively, a number being simply normal means that no digit occurs more frequently than any other. If a number is normal, no finite combination of digits of a given length occurs more frequently than any other combination of the same length. A normal number can be thought of as an infinite sequence of coin flips (binary) or rolls of a die (base 6). Even though there will be sequences such as 10, 100, or more consecutive tails (binary) or fives (base 6) or even 10, 100, or more repetitions of a sequence such as tail-head (two consecutive coin flips) or 6-1 (two consecutive rolls of a die), there will also be equally many of any other sequence of equal length. No digit or sequence is "favored".
A number is said to be normal (sometimes called absolutely normal) if it is normal in all integer bases greater than or equal to 2.
While a general proof can be given that almost all real numbers are normal (meaning that the set of non-normal numbers has Lebesgue measure zero), this proof is not constructive, and only a few specific numbers have been shown to be normal. For example, any Chaitin's constant is normal (and uncomputable). It is widely believed that the (computable) numbers , , and e are normal, but a proof remains elusive.
Definitions
Let be a finite alphabet of -digits, the set of all infinite sequences that may be drawn from that alphabet, and the set of finite sequences, or strings. Let be such a sequence. For each in let denote the number of times the digit appears in the first digits of the sequence . We say that is simply normal if the limit
for each . Now let be any finite string in and let be the |
https://en.wikipedia.org/wiki/Saliva | Saliva (commonly referred to as spit) is an extracellular fluid produced and secreted by salivary glands in the mouth. In humans, saliva is around 99% water, plus electrolytes, mucus, white blood cells, epithelial cells (from which DNA can be extracted), enzymes (such as lipase and amylase), antimicrobial agents (such as secretory IgA, and lysozymes).
The enzymes found in saliva are essential in beginning the process of digestion of dietary starches and fats. These enzymes also play a role in breaking down food particles entrapped within dental crevices, thus protecting teeth from bacterial decay. Saliva also performs a lubricating function, wetting food and permitting the initiation of swallowing, and protecting the oral mucosa from drying out.
Various animal species have special uses for saliva that go beyond predigestion. Some swifts use their gummy saliva to build nests. Aerodramus nests form the basis of bird's nest soup.
Cobras, vipers, and certain other members of the venom clade hunt with venomous saliva injected by fangs. Some caterpillars produce silk fiber from silk proteins stored in modified salivary glands (which are unrelated to the vertebrate ones).
Composition
Produced in salivary glands, human saliva comprises 99.5% water, but also contains many important substances, including electrolytes, mucus, antibacterial compounds and various enzymes. Medically, constituents of saliva can noninvasively provide important diagnostic information related to oral and systemic diseases.
Water: 99.5%
Electrolytes:
2–21 mmol/L sodium (lower than blood plasma)
10–36 mmol/L potassium (higher than plasma)
1.2–2.8 mmol/L calcium (similar to plasma)
0.08–0.5 mmol/L magnesium
5–40 mmol/L chloride (lower than plasma)
25 mmol/L bicarbonate (higher than plasma)
1.4–39 mmol/L phosphate
Iodine (mmol/L concentration is usually higher than plasma, but dependent variable according to dietary iodine intake)
Mucus (mucus in saliva mainly consists of mucopolysacchari |
https://en.wikipedia.org/wiki/Intensive%20and%20extensive%20properties | Physical or chemical properties of materials and systems can often be categorized as being either intensive or extensive, according to how the property changes when the size (or extent) of the system changes.
The terms "intensive and extensive quantities" were introduced into physics by German mathematician Georg Helm in 1898, and by American physicist and chemist Richard C. Tolman in 1917.
According to International Union of Pure and Applied Chemistry (IUPAC), an intensive property or intensive quantity is one whose magnitude is independent of the size of the system.
An intensive property is not necessarily homogeneously distributed in space; it can vary from place to place in a body of matter and radiation. Examples of intensive properties include temperature, T; refractive index, n; density, ρ; and hardness, η.
By contrast, an extensive property or extensive quantity is one whose magnitude is additive for subsystems.
Examples include mass, volume and entropy.
Not all properties of matter fall into these two categories. For example, the square root of the volume is neither intensive nor extensive. If a system is doubled in size by juxtaposing a second identical system, the value of an intensive property equals the value for each subsystem and the value of an extensive property is twice the value for each subsystem. However the property √V is instead multiplied by √2 .
Intensive properties
An intensive property is a physical quantity whose value does not depend on the amount of substance which was measured. The most obvious intensive quantities are ratios of extensive quantities. In a homogeneous system divided into two halves, all its extensive properties, in particular its volume and its mass, are divided into two halves. All its intensive properties, such as the mass per volume (mass density) or volume per mass (specific volume), must remain the same in each half.
The temperature of a system in thermal equilibrium is the same as the temperature of any part |
https://en.wikipedia.org/wiki/Translation%20%28geometry%29 | In Euclidean geometry, a translation is a geometric transformation that moves every point of a figure, shape or space by the same distance in a given direction. A translation can also be interpreted as the addition of a constant vector to every point, or as shifting the origin of the coordinate system. In a Euclidean space, any translation is an isometry.
As a function
If is a fixed vector, known as the translation vector, and is the initial position of some object, then the translation function will work as .
If is a translation, then the image of a subset under the function is the translate of by . The translate of by is often written .
Horizontal and vertical translations
In geometry, a vertical translation (also known as vertical shift) is a translation of a geometric object in a direction parallel to the vertical axis of the Cartesian coordinate system.
Often, vertical translations are considered for the graph of a function. If f is any function of x, then the graph of the function f(x) + c (whose values are given by adding a constant c to the values of f) may be obtained by a vertical translation of the graph of f(x) by distance c. For this reason the function f(x) + c is sometimes called a vertical translate of f(x). For instance, the antiderivatives of a function all differ from each other by a constant of integration and are therefore vertical translates of each other.
In function graphing, a horizontal translation is a transformation which results in a graph that is equivalent to shifting the base graph left or right in the direction of the x-axis. A graph is translated k units horizontally by moving each point on the graph k units horizontally.
For the base function f(x) and a constant k, the function given by g(x) = f(x − k), can be sketched f(x) shifted k units horizontally.
If function transformation was talked about in terms of geometric transformations it may be clearer why functions translate horizontally the way they do. When ad |
https://en.wikipedia.org/wiki/List%20of%20mathematical%20probabilists | See probabilism for the followers of such a theory in theology or philosophy.
This list contains only probabilists in the sense of mathematicians specializing in probability theory.
This list is incomplete; please add to it.
David Aldous (born 1952)
Siva Athreya
Thomas Bayes (1702–1761) - British mathematician and Presbyterian minister, known for Bayes' theorem
Gerard Ben-Arous (born 1957) - Courant Institute of Mathematical Sciences
Itai Benjamini
Jakob Bernoulli (1654–1705) - Switzerland, known for Bernoulli trials
Joseph Louis François Bertrand (1822–1900)
Abram Samoilovitch Besicovitch (1891–1970)
Patrick Billingsley (1925–2011)
Erwin Bolthausen (born 1945)
Carlo Emilio Bonferroni (1892–1960)
Émile Borel (1871–1956)
Sourav Chatterjee
Kai Lai Chung (1917–2009)
Erhan Cinlar (born 1941)
Harald Cramér (1893–1985)
Amir Dembo (born 1958)
Persi Diaconis (born 1945)
Hugo Duminil-Copin (born 1985)
Joseph Leo Doob (1910–2004)
Lester Dubins (1920–2010)
Eugene Dynkin (1924–2014)
Robert J. Elliott (born 1940)
Paul Erdős (1913–1996)
Alison Etheridge (born 1964)
Steve Evans (born 1960)
William Feller (1906–1970)
Bruno de Finetti (1906–1985) - Italian probabilist and statistician
Geoffrey Grimmett (born 1950)
Alice Guionnet (born 1969)
Ian Hacking (born 1936)
Paul Halmos (1916–2006)
Joseph Halpern (born 1953)
David Heath (c.1943–2011)
Wassily Hoeffding (1914–1991)
Kiyoshi Itô (1915–2008)
Jean Jacod (1944–)
Edwin Thompson Jaynes (1922–1998)
Mark Kac (1914–1984)
Olav Kallenberg (born 1939)
Rudolf E. Kálmán (1930–2016)
Samuel Karlin (1924–2007)
David George Kendall (1918–2007)
Richard Kenyon (born 1964) - Yale University
Harry Kesten (1931–2019)
John Maynard Keynes (1883–1946) - best known for his pioneering work in economics
Aleksandr Khinchin (1894–1959)
Andrey Kolmogorov (1903–1987)
Pierre-Simon Laplace (1749–1827)
Gregory Lawler (born 1955)
Lucien Le Cam (1924–2000)
Jean-François Le Gall (born 1959)
Paul Lévy (1886–1971)
Jarl Waldemar Lindeberg (1876–1932)
Andrey Markov (18 |
https://en.wikipedia.org/wiki/Drill | A drill is a tool used for making round holes or driving fasteners. It is fitted with a bit, either a drill or driver chuck. Hand-operated types are dramatically decreasing in popularity and cordless battery-powered ones proliferating due to increased efficiency and ease of use.
Drills are commonly used in woodworking, metalworking, construction, machine tool fabrication, construction and utility projects. Specially designed versions are made for miniature applications.
History
Around 35,000 BC, Homo sapiens discovered the benefits of the application of rotary tools. This would have rudimentarily consisted of a pointed rock being spun between the hands to bore a hole through another material. This led to the hand drill, a smooth stick, that was sometimes attached to flint point, and was rubbed between the palms. This was used by many ancient civilizations around the world including the Mayans. The earliest perforated artifacts, such as bone, ivory, shells, and antlers found, are from the Upper Paleolithic era. Bow drill (strap-drill) are the first machine drills, as they convert a back and forth motion to a rotary motion, and they can be traced back to around 10,000 years ago. It was discovered that tying a cord around a stick, and then attaching the ends of the string to the ends of a stick (a bow), allowed a user to drill quicker and more efficiently. Mainly used to create fire, bow-drills were also used in ancient woodwork, stonework, and dentistry. Archaeologists discovered a Neolithic grave yard in Mehrgarh, Pakistan, dating from the time of the Harappans, around 7,500–9,000 years ago, containing nine adult bodies with a total of eleven teeth that had been drilled. There are hieroglyphs depicting Egyptian carpenters and bead makers in a tomb at Thebes using bow-drills. The earliest evidence of these tools being used in Egypt dates back to around 2500 BCE. The usage of bow-drills was widely spread through Europe, Africa, Asia, and North America, during a |
https://en.wikipedia.org/wiki/Dysgeusia | Dysgeusia, also known as parageusia, is a distortion of the sense of taste. Dysgeusia is also often associated with ageusia, which is the complete lack of taste, and hypogeusia, which is a decrease in taste sensitivity. An alteration in taste or smell may be a secondary process in various disease states, or it may be the primary symptom. The distortion in the sense of taste is the only symptom, and diagnosis is usually complicated since the sense of taste is tied together with other sensory systems. Common causes of dysgeusia include chemotherapy, asthma treatment with albuterol, and zinc deficiency. Liver disease, hypothyroidism, and rarely certain types of seizures can also lead to dysgeusia. Different drugs could also be responsible for altering taste and resulting in dysgeusia. Due to the variety of causes of dysgeusia, there are many possible treatments that are effective in alleviating or terminating the symptoms of dysgeusia. These include artificial saliva, pilocarpine, zinc supplementation, alterations in drug therapy, and alpha lipoic acid.
Signs and symptoms
The alterations in the sense of taste, usually a metallic taste, and sometimes smell are the only symptoms.
Causes
Chemotherapy
A major cause of dysgeusia is chemotherapy for cancer. Chemotherapy often induces damage to the oral cavity, resulting in oral mucositis, oral infection, and salivary gland dysfunction. Oral mucositis consists of inflammation of the mouth, along with sores and ulcers in the tissues. Healthy individuals normally have a diverse range of microbial organisms residing in their oral cavities; however, chemotherapy can permit these typically non-pathogenic agents to cause serious infection, which may result in a decrease in saliva. In addition, patients who undergo radiation therapy also lose salivary tissues. Saliva is an important component of the taste mechanism. Saliva both interacts with and protects the taste receptors in the mouth. Saliva mediates sour and sweet tastes thr |
https://en.wikipedia.org/wiki/Progeria | Progeria is a specific type of progeroid syndrome, also known as Hutchinson–Gilford syndrome. A single gene mutation is responsible for progeria. The gene, known as lamin A (LMNA), makes a protein necessary for holding the nucleus of the cell together. When this gene gets mutated, an abnormal form of lamin A protein called progerin is produced. Progeroid syndromes are a group of diseases that causes individuals to age faster than usual, leading to them appearing older than they actually are. Patients born with progeria typically live to an age of mid-teens to early twenties.
Severe cardiovascular complications usually develop by puberty, resulting in death.
Signs and symptoms
Children with progeria usually develop the first symptoms during their first few months of life. The earliest symptoms may include a failure to thrive and a localized scleroderma-like skin condition. As a child ages past infancy, additional conditions become apparent, usually around 18–24 months. Limited growth, full-body alopecia (hair loss), and a distinctive appearance (a small face with a shallow, recessed jaw and a pinched nose) are all characteristics of progeria. Signs and symptoms of this progressive disease tend to become more marked as the child ages. Later, the condition causes wrinkled skin, kidney failure, loss of eyesight, and atherosclerosis and other cardiovascular problems. Scleroderma, a hardening and tightening of the skin on trunk and extremities of the body, is prevalent. People diagnosed with this disorder usually have small, fragile bodies, like those of older adults. The head is usually large relative to the body, with a narrow, wrinkled face and a beak nose. Prominent scalp veins are noticeable (made more obvious by alopecia), as well as prominent eyes. Musculoskeletal degeneration causes loss of body fat and muscle, stiff joints, hip dislocations, and other symptoms generally absent in the non-elderly population. Individuals usually retain typical mental and motor f |
https://en.wikipedia.org/wiki/Binary%20classification | Binary classification is the task of classifying the elements of a set into two groups (each called class) on the basis of a classification rule. Typical binary classification problems include:
Medical testing to determine if a patient has certain disease or not;
Quality control in industry, deciding whether a specification has been met;
In information retrieval, deciding whether a page should be in the result set of a search or not.
Binary classification is dichotomization applied to a practical situation. In many practical binary classification problems, the two groups are not symmetric, and rather than overall accuracy, the relative proportion of different types of errors is of interest. For example, in medical testing, detecting a disease when it is not present (a false positive) is considered differently from not detecting a disease when it is present (a false negative).
Statistical binary classification
Statistical classification is a problem studied in machine learning. It is a type of supervised learning, a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. When there are only two categories the problem is known as statistical binary classification.
Some of the methods commonly used for binary classification are:
Decision trees
Random forests
Bayesian networks
Support vector machines
Neural networks
Logistic regression
Probit model
Genetic Programming
Multi expression programming
Linear genetic programming
Each classifier is best in only a select domain based upon the number of observations, the dimensionality of the feature vector, the noise in the data and many other factors. For example, random forests perform better than SVM classifiers for 3D point clouds.
Evaluation of binary classifiers
There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific me |
https://en.wikipedia.org/wiki/Split%20horizon%20route%20advertisement | In computer networking, split-horizon route advertisement is a method of preventing routing loops in distance-vector routing protocols by prohibiting a router from advertising a route back onto the interface from which it was learned.
The concept was suggested in 1974 by Torsten Cegrell, and originally implemented in the ARPANET-inspired Swedish network TIDAS.
Terminology
Here is some basic terminology:
Route poisoning: if a node N learns that its route to a destination D is unreachable, inform that to all nodes in the network by sending them a message stating that the distance from N to D, as perceived by N, is infinite.
Split horizon rule: if a node N uses interface I to transmit to a given destination D, N should not send through I new information about D.
Poison reverse rule: if a node N uses interface I to transmit to a given destination D, N sends through I the information that its cost-to-go to D is infinite.
Whereas under split horizon N does not send any information through I, under poison reverse node N tells a white-lie.
Example
In this example, network node A routes packets to node B in order to reach node C. The links between the nodes are distinct point-to-point links.
According to the split-horizon rule, node A does not advertise its route for C (namely A to B to C) back to B. On the surface, this seems redundant since B will never route via node A because the route costs more than the direct route from B to C. However, if the link between B and C goes down, and B had received a route from A to C, B could end up using that route via A. A would send the packet right back to B, creating a loop. This is the Count to Infinity Problem. With the split-horizon rule in place, this particular loop scenario cannot happen, improving convergence time in complex, highly-redundant environments.
Split-horizon routing with poison reverse is a variant of split-horizon route advertising in which a router actively advertises routes as unreachable over the |
https://en.wikipedia.org/wiki/Human%20microbiome | The human microbiome is the aggregate of all microbiota that reside on or within human tissues and biofluids along with the corresponding anatomical sites in which they reside, including the skin, mammary glands, seminal fluid, uterus, ovarian follicles, lung, saliva, oral mucosa, conjunctiva, biliary tract, and gastrointestinal tract. Types of human microbiota include bacteria, archaea, fungi, protists, and viruses. Though micro-animals can also live on the human body, they are typically excluded from this definition. In the context of genomics, the term human microbiome is sometimes used to refer to the collective genomes of resident microorganisms; however, the term human metagenome has the same meaning.
Humans are colonized by many microorganisms, with approximately the same order of magnitude of non-human cells as human cells. Some microorganisms that colonize humans are commensal, meaning they co-exist without harming humans; others have a mutualistic relationship with their human hosts. Conversely, some non-pathogenic microorganisms can harm human hosts via the metabolites they produce, like trimethylamine, which the human body converts to trimethylamine N-oxide via FMO3-mediated oxidation. Certain microorganisms perform tasks that are known to be useful to the human host, but the role of most of them is not well understood. Those that are expected to be present, and that under normal circumstances do not cause disease, are sometimes deemed normal flora or normal microbiota.
During early life, the establishment of a diverse and balanced human microbiota plays a critical role in shaping an individual's long-term health. Studies have shown that the composition of the gut microbiota during infancy is influenced by various factors, including mode of delivery, breastfeeding, and exposure to environmental factors. There are several beneficial species of bacteria and potential probiotics present in breast milk. Research has highlighted the beneficial effects of a |
https://en.wikipedia.org/wiki/George%20Peacock | George Peacock FRS (9 April 1791 – 8 November 1858) was an English mathematician and Anglican cleric. He founded what has been called the British algebra of logic.
Early life
Peacock was born on 9 April 1791 at Thornton Hall, Denton, near Darlington, County Durham. His father, Thomas Peacock, was a priest of the Church of England, incumbent and for 50 years curate of the parish of Denton, where he also kept a school. In early life Peacock did not show any precocity of genius, and was more remarkable for daring feats of climbing than for any special attachment to study. Initially, he received his elementary education from his father and then at Sedbergh School, and at 17 years of age, he was sent to Richmond School under James Tate, a graduate of Cambridge University. At this school he distinguished himself greatly both in classics and in the rather elementary mathematics then required for entrance at Cambridge. In 1809 he became a student of Trinity College, Cambridge.
In 1812 Peacock took the rank of Second Wrangler, and the second Smith's prize, the senior wrangler being John Herschel. Two years later he became a candidate for a fellowship in his college and won it immediately, partly by means of his extensive and accurate knowledge of the classics. A fellowship then meant about 200 pounds a year, tenable for seven years provided the Fellow did not marry meanwhile, and capable of being extended after the seven years provided the Fellow took clerical orders, which Peacock did in 1819.
Mathematical career
The year after taking a Fellowship, Peacock was appointed a tutor and lecturer of his college, which position he continued to hold for many years. Peacock, in common with many other students of his own standing, was profoundly impressed with the need of reforming Cambridge's position ignoring the differential notation for calculus, and while still an undergraduate formed a league with Babbage and Herschel to adopt measures to bring it about. In 1815 they formed |
https://en.wikipedia.org/wiki/Drug%20resistance | Drug resistance is the reduction in effectiveness of a medication such as an antimicrobial or an antineoplastic in treating a disease or condition. The term is used in the context of resistance that pathogens or cancers have "acquired", that is, resistance has evolved. Antimicrobial resistance and antineoplastic resistance challenge clinical care and drive research. When an organism is resistant to more than one drug, it is said to be multidrug-resistant.
The development of antibiotic resistance in particular stems from the drugs targeting only specific bacterial molecules (almost always proteins). Because the drug is so specific, any mutation in these molecules will interfere with or negate its destructive effect, resulting in antibiotic resistance. Furthermore, there is mounting concern over the abuse of antibiotics in the farming of livestock, which in the European Union alone accounts for three times the volume dispensed to humans – leading to development of super-resistant bacteria.
Bacteria are capable of not only altering the enzyme targeted by antibiotics, but also by the use of enzymes to modify the antibiotic itself and thus neutralize it. Examples of target-altering pathogens are Staphylococcus aureus, vancomycin-resistant enterococci and macrolide-resistant Streptococcus, while examples of antibiotic-modifying microbes are Pseudomonas aeruginosa and aminoglycoside-resistant Acinetobacter baumannii.
In short, the lack of concerted effort by governments and the pharmaceutical industry, together with the innate capacity of microbes to develop resistance at a rate that outpaces development of new drugs, suggests that existing strategies for developing viable, long-term anti-microbial therapies are ultimately doomed to failure. Without alternative strategies, the acquisition of drug resistance by pathogenic microorganisms looms as possibly one of the most significant public health threats facing humanity in the 21st century. Some of the best alternative so |
https://en.wikipedia.org/wiki/Mac%20OS%209 | Mac OS 9 is the ninth and final major release of Apple's classic Mac OS operating system which was succeeded by Mac OS X (renamed to OS X in 2011 and macOS in 2016) in 2001. Introduced on October 23, 1999, it was promoted by Apple as "The Best Internet Operating System Ever", highlighting Sherlock 2's Internet search capabilities, integration with Apple's free online services known as iTools and improved Open Transport networking. While Mac OS 9 lacks protected memory and full pre-emptive multitasking, lasting improvements include the introduction of an automated Software Update engine and support for multiple users.
Apple discontinued development of Mac OS 9 in late 2001, transitioning all future development to Mac OS X. The final updates to Mac OS 9 addressed compatibility issues with Mac OS X while running in the Classic Environment and compatibility with Carbon applications. At the 2002 Worldwide Developers Conference, Steve Jobs began his keynote address by staging a mock funeral for OS 9.
Features
Apple billed Mac OS 9 as including "50 new features" and heavily marketed its Sherlock 2 software, which introduced a "channels" feature for searching different online resources and introduced a QuickTime-like metallic appearance. Mac OS 9 also featured integrated support for Apple's suite of Internet services known as iTools (later re-branded as .Mac, then MobileMe, which was replaced by iCloud) and included improved TCP/IP functionality with Open Transport 2.5.
Other features new to Mac OS 9 include:
Integrated support for multiple user accounts without using At Ease.
Support for voice login through VoicePrint passwords.
Keychain, a feature allowing users to save passwords and textual data encrypted in protected keychains.
A Software Update control panel for automatic download and installation of Apple system software updates.
A redesigned Sound control panel and support for USB audio.
Speakable Items 2.0, also known as PlainTalk, featuring improved speech synt |
https://en.wikipedia.org/wiki/Functional%20dependency | In relational database theory, a functional dependency is a constraint between two sets of attributes in a relation from a database. In other words, a functional dependency is a constraint between two attributes in a relation.
Given a relation R and sets of attributes , X is said to functionally determine Y (written X → Y) if and only if each X value in R is associated with precisely one Y value in R; R is then said to satisfy the functional dependency X → Y. Equivalently, the projection is a function, i.e. Y is a function of X. In simple words, if the values for the X attributes are known (say they are x), then the values for the Y attributes corresponding to x can be determined by looking them up in any tuple of R containing x. Customarily X is called the determinant set and Y the dependent set. A functional dependency FD: X → Y is called trivial if Y is a subset of X.
In other words, a dependency FD: X → Y means that the values of Y are determined by the values of X. Two tuples sharing the same values of X will necessarily have the same values of Y.
The determination of functional dependencies is an important part of designing databases in the relational model, and in database normalization and denormalization. A simple application of functional dependencies is Heath's theorem; it says that a relation R over an attribute set U and satisfying a functional dependency X → Y can be safely split in two relations having the lossless-join decomposition property, namely into where Z = U − XY are the rest of the attributes. (Unions of attribute sets are customarily denoted by there juxtapositions in database theory.) An important notion in this context is a candidate key, defined as a minimal set of attributes that functionally determine all of the attributes in a relation. The functional dependencies, along with the attribute domains, are selected so as to generate constraints that would exclude as much data inappropriate to the user domain from the system as possibl |
https://en.wikipedia.org/wiki/University%20of%20Kent | The University of Kent (formerly the University of Kent at Canterbury, abbreviated as UKC) is a semi-collegiate public research university based in Kent, United Kingdom. The university was granted its royal charter on 4 January 1965 and the following year Princess Marina, Duchess of Kent, was formally installed as the first Chancellor.
The university has its main campus north of Canterbury situated within of park land, housing over 6,000 students, as well as campuses in Medway and Tonbridge in Kent and European postgraduate centres in Brussels, Athens, Rome and Paris. The university is international, with students from 158 different nationalities and 41% of its academic and research staff being from outside the United Kingdom. It is a member of the Santander Network of European universities encouraging social and economic development.
History
Origins
A university in the city of Canterbury was first considered in 1947, when an anticipated growth in student numbers led several residents to seek the creation of a new university, including Kent. However, the plans never came to fruition. A decade later both population growth and greater demand for university places led to a re-consideration. In 1959 the Education Committee of Kent County Council explored the creation of a new university, formally accepting the proposal unanimously on 24 February 1960. Two months later the Education Committee agreed to seek a site at or near Canterbury, given the historical associations of the city, subject to the support of Canterbury City Council.
By 1962 a site was found at Beverley Farm, straddling the then boundary between the City of Canterbury and the administrative county of Kent. The university's original name, chosen in 1962, was the University of Kent at Canterbury, reflecting the fact that the campus straddled the boundary between the county borough of Canterbury and Kent County Council. At the time it was the normal practice for universities to be named after the town |
https://en.wikipedia.org/wiki/Emitter-coupled%20logic | In electronics, emitter-coupled logic (ECL) is a high-speed integrated circuit bipolar transistor logic family. ECL uses an overdriven bipolar junction transistor (BJT) differential amplifier with single-ended input and limited emitter current to avoid the saturated (fully on) region of operation and its slow turn-off behavior.
As the current is steered between two legs of an emitter-coupled pair, ECL is sometimes called current-steering logic (CSL),
current-mode logic (CML)
or current-switch emitter-follower (CSEF) logic.
In ECL, the transistors are never in saturation, the input and output voltages have a small swing (0.8 V), the input impedance is high and the output impedance is low. As a result, the transistors change states quickly, gate delays are low, and the fanout capability is high. In addition, the essentially constant current draw of the differential amplifiers minimises delays and glitches due to supply-line inductance and capacitance, and the complementary outputs decrease the propagation time of the whole circuit by reducing inverter count.
ECL's major disadvantage is that each gate continuously draws current, which means that it requires (and dissipates) significantly more power than those of other logic families, especially when quiescent.
The equivalent of emitter-coupled logic made from FETs is called source-coupled logic (SCFL).
A variation of ECL in which all signal paths and gate inputs are differential is known as differential current switch (DCS) logic.
History
ECL was invented in August 1956 at IBM by Hannon S. Yourke. Originally called current-steering logic, it was used in the Stretch, IBM 7090, and IBM 7094 computers. The logic was also called a current-mode circuit. It was also used to make the ASLT circuits in the IBM 360/91.
Yourke's current switch was a differential amplifier whose input logic levels were different from the output logic levels. "In current mode operation, however, the output signal consists of voltage level |
https://en.wikipedia.org/wiki/Zaxxon | is an isometric shooter arcade video game, developed and released by Sega in 1982. The player pilots a ship through heavily defended space fortresses. Japanese electronics company Ikegami Tsushinki also developed the game.
Zaxxon was the first game to employ axonometric projection, which lent its name to the game (AXXON from AXONometric projection). The type of axonometric projection is isometric projection: this effect simulates three dimensions from a third-person viewpoint. It was also the first arcade game to be advertised on television, with a commercial produced by Paramount Pictures for $150,000. The game was a critical and commercial success upon release, becoming one of the top five highest-grossing arcade games of 1982 in the United States. Sega followed it up with the arcade sequel Super Zaxxon (1982) and Zaxxon-like shooter Future Spy (1984), in addition to the non-scrolling isometric platform game Congo Bongo (1983).
Gameplay
The objective of the game is to hit as many targets as possible without being shot down or running out of fuel—which can be replenished, paradoxically, by blowing up fuel drums (300 points). There are two fortresses to fly through, with an outer space segment between them. At the end of the second fortress is a boss in the form of the Zaxxon robot.
The player's ship casts a shadow to indicate its height. An altimeter is also displayed; in space there is nothing for the ship to cast a shadow on. The walls at the entrance and exit of each fortress have openings that the ship must be at the right altitude to pass through. Within each fortress are additional walls that the ship's shadow and altimeter aid in flying over successfully.
The game is controlled by a four-directional joystick. On arcade cabinets this is an aircraft-type stick with a molded hand grip. Pushing forward makes the player's aircraft lower in altitude and pulling back makes it rise. The aircraft cannot move forward or backward; it flies at constant speed. As t |
https://en.wikipedia.org/wiki/Discrete%20Hartley%20transform | A discrete Hartley transform (DHT) is a Fourier-related transform of discrete, periodic data similar to the discrete Fourier transform (DFT), with analogous applications in signal processing and related fields. Its main distinction from the DFT is that it transforms real inputs to real outputs, with no intrinsic involvement of complex numbers. Just as the DFT is the discrete analogue of the continuous Fourier transform (FT), the DHT is the discrete analogue of the continuous Hartley transform (HT), introduced by Ralph V. L. Hartley in 1942.
Because there are fast algorithms for the DHT analogous to the fast Fourier transform (FFT), the DHT was originally proposed by Ronald N. Bracewell in 1983 as a more efficient computational tool in the common case where the data are purely real. It was subsequently argued, however, that specialized FFT algorithms for real inputs or outputs can ordinarily be found with slightly fewer operations than any corresponding algorithm for the DHT.
Definition
Formally, the discrete Hartley transform is a linear, invertible function H: Rn → Rn (where R denotes the set of real numbers). The N real numbers x0, ..., xN−1 are transformed into the N real numbers H0, ..., HN−1 according to the formula
The combination is sometimes denoted , and should not be confused with , or which appears in the DFT definition (where i is the imaginary unit).
As with the DFT, the overall scale factor in front of the transform and the sign of the sine term are a matter of convention. Although these conventions occasionally vary between authors, they do not affect the essential properties of the transform.
Properties
The transform can be interpreted as the multiplication of the vector (x0, ...., xN−1) by an N-by-N matrix; therefore, the discrete Hartley transform is a linear operator. The matrix is invertible; the inverse transformation, which allows one to recover the xn from the Hk, is simply the DHT of Hk multiplied by 1/N. That is, the DHT is its ow |
https://en.wikipedia.org/wiki/SMART-1 | SMART-1 was a Swedish-designed European Space Agency satellite that orbited the Moon. It was launched on 27 September 2003 at 23:14 UTC from the Guiana Space Centre in Kourou, French Guiana. "SMART-1" stands for Small Missions for Advanced Research in Technology-1. On 3 September 2006 (05:42 UTC), SMART-1 was deliberately crashed into the Moon's surface, ending its mission.
Spacecraft design
SMART-1 was about one meter across (3.3 ft), and lightweight in comparison to other probes. Its launch mass was 367 kg or 809 pounds, of which 287 kg (633 lb) was non-propellant.
It was propelled by a solar-powered Hall effect thruster (Snecma PPS-1350-G) using 82 Kg of xenon gas contained in a 50 litres tank at a pressure of 150 bar at launch. The ion engine thruster used an electrostatic field to ionize the xenon and accelerate the ions achieving a specific impulse of 16.1 kN·s/kg (1,640 seconds), more than three times the maximum for chemical rockets. One kg of propellant (1/350 to 1/300 of the total mass of the spacecraft) produced a delta-v of about 45 m/s. The electric propulsion subsystem weighted 29 kg with a peak power consumption of 1,200 watts. SMART-1 was the first in the program of ESA's Small Missions for Advanced Research and Technology.
The solar arrays made capable of 1850W at the beginning of the mission, were able to provide the maximum set of 1,190 W to the thruster, giving a nominal thrust of 68 mN, hence an acceleration of 0.2 mm/s2 or 0.7 m/s per hour (i.e., just under 0.00002 g of acceleration). As with all ion-engine powered craft, orbital maneuvers were not carried out in short bursts but very gradually. The particular trajectory taken by SMART-1 to the Moon required thrusting for about one third to one half of every orbit. When spiraling away from the Earth thrusting was done on the perigee part of the orbit. At the end of the mission, the thruster had demonstrated the following capability:
Thruster operating time: 5000 h
Xenon throughput: 82 kg |
https://en.wikipedia.org/wiki/Biocoenosis | A biocenosis (UK English, biocoenosis, also biocenose, biocoenose, biotic community, biological community, ecological community, life assemblage), coined by Karl Möbius in 1877, describes the interacting organisms living together in a habitat (biotope). The use of this term has declined in the 21st сentury.
In the palaeontological literature, the term distinguishes "life assemblages", which reflect the original living community, living together at one place and time. In other words, it is an assemblage of fossils or a community of specific time, which is different from "death assemblages" (thanatocoenoses). No palaeontological assemblage will ever completely represent the original biological community (i.e. the biocoenosis, in the sense used by an ecologist); the term thus has somewhat different meanings in a palaeontological and an ecological context.
Based on the concept of biocenosis, ecological communities can take various forms:
Zoocenosis for the faunal community,
Phytocenosis for the flora community,
Microbiocenosis for the microbial community.
The geographical extent of a biocenose is limited by the requirement of a more or less uniform species composition.
Ecosystems
An ecosystem, originally defined by Tansley (1935), is a biotic community (or biocenosis) along with its physical environment (or biotope). In ecological studies, biocenosis is the emphasis on relationships between species in an area. These relationships are an additional consideration to the interaction of each species with the physical environment.
Biotic communities
Biotic communities vary in size, and larger ones may contain smaller ones. Species interactions are evident in food or feeding relationships. A method of delineating biotic communities is to map the food network to identify which species feed upon which others and then determine the system boundary as the one that can be drawn through the fewest consumption links relative to the number of species within the boundary.
Map |
https://en.wikipedia.org/wiki/Ecoregion%20conservation%20status | Conservation status is a measure used in conservation biology to assess an ecoregion's degree of habitat alteration and habitat conservation. It is used to set priorities for conservation.
Conservation status and biological distinctiveness were the two measures used by the World Wildlife Fund (WWF) to develop the Global 200, a list of high-priority ecoregions for conservation, and for the WWF's conservation assessments at continent (or biogeographic realm) scale.
Ecoregions are classified into one of three broad categories: "critical/endangered" (CE), "vulnerable" (V), or "relatively stable/relatively intact" (RS). The WWF's conservation status index is determined by analyzing four factors:
Habitat loss is the percentage of an ecoregion's habitat that has been converted to agriculture or urban areas;
Habitat blocks measures of the size of remaining habitat blocks;
Habitat fragmentation is the degree to which remaining habitat is fragmented, measured as the ratio of the total perimeter of remaining habitat blocks to their total area;
Habitat protection measures the area of remaining habitat in protected areas, and the degree of protection provided (IUCN protected area categories).
Additional factors considered for the Global 200 include degree of habitat degradation, degree of protection needed, degree of urgency for conservation needs, and types of conservation practiced or required.
See also
List of global 200 ecoregions
References
Conservation biology
Ecoregions
Landscape ecology |
https://en.wikipedia.org/wiki/Phagocytosis | Phagocytosis () is the process by which a cell uses its plasma membrane to engulf a large particle (≥ 0.5 μm), giving rise to an internal compartment called the phagosome. It is one type of endocytosis. A cell that performs phagocytosis is called a phagocyte.
In a multicellular organism's immune system, phagocytosis is a major mechanism used to remove pathogens and cell debris. The ingested material is then digested in the phagosome. Bacteria, dead tissue cells, and small mineral particles are all examples of objects that may be phagocytized. Some protozoa use phagocytosis as means to obtain nutrients.
History
The history of phagocytosis represents the scientific establishment of immunology as the process is the first immune response mechanism discovered and understood as such. The earliest definitive account of cell eating was given by Swiss scientist Albert von Kölliker in 1849. In his report in Zeitschrift für Wissenschaftliche Zoologie, Kölliker described the feeding process of an amoeba-like alga, Actinophyrys sol (a heliozoan) mentioning details of how the protist engulfed and swallowed (the process now called endocytosis) a small organism, that he named infusoria (a generic name for microbes at the time).
The first demonstration of phagocytosis as a property of leucocytes, the immune cells, was from the German zoologist Ernst Haeckel. Haeckel discovered that blood cells of sea slug,Tethys, could ingest Indian ink (or indigo) particles. It was the first direct evidence of phagocytosis by immune cells. Haeckel reported his experiment in a 1862 monograph Die Radiolarien (Rhizopoda Radiaria): Eine Monographie.
Phagocytosis was noted by Canadian physician William Osler (1876), and later studied and named by Élie Metchnikoff (1880, 1883).
In immune system
Phagocytosis is one main mechanisms of the innate immune defense. It is one of the first processes responding to infection, and is also one of the initiating branches of an adaptive immune response. Altho |
https://en.wikipedia.org/wiki/UNIVAC%201105 | The UNIVAC 1105 was a follow-on computer to the UNIVAC 1103A introduced by Sperry Rand in September 1958. The UNIVAC 1105 used 21 types of vacuum tubes, 11 types of diodes, 10 types of transistors, and three core types.
The UNIVAC 1105 had either 8,192 or 12,288 words of 36-bit magnetic core memory, in two or three banks of 4,096 words each. Magnetic drum memory provided either 16,384 or 32,768 words, in one or two drums with 16,384 words each. Sixteen to twenty-four UNISERVO II tape drives were connected, with a maximum capacity (not counting block overhead) of 1,200,000 words per tape.
Fixed-point numbers had a one-bit sign and a 35-bit value, with negative values represented in ones' complement format. Floating-point numbers had a one-bit sign, an eight-bit characteristic, and a 27-bit mantissa. Instructions had a six-bit operation code and two 15-bit operand addresses.
A complete UNIVAC 1105 computer system required 160 kW of power (175 KVA, 0.9 power factor) and an air conditioning unit with a power of at least 35 tons (123 kW) for cooling input water. The computer system weighed about with a floor loading of 47 lb/ft2 (230 kg/m2) and required a room 49 x 64 x 10 ft (15 x 20 x 3 m). The floor space for the computer was approximately 3,752 ft2 (350 m2). The power, refrigeration and equipment room was approximately 2,450 ft2 (230 m2).
Cost, price and rental rates
Chapel Hill
In 1959, a Univac 1105 located in the basement of Phillips Hall of The University of North Carolina at Chapel Hill was one of three computers of its type. It was intended primarily for the United States Census Bureau, which had one of its own; Armour Institute of Technology had the other. The Chapel Hill unit cost $2.4 million, with the improvements to the basement, including 16-inch concrete walls to protect it from nuclear attack, added $1.2 million. Its memory was less than 50 kilobytes, or one 8 1/2 x 11 document, with the capability of adding 30,000 numbers per second. The Univac |
https://en.wikipedia.org/wiki/UNIVAC%201103 | The UNIVAC 1103 or ERA 1103, a successor to the UNIVAC 1101, was a computer system designed by Engineering Research Associates and built by the Remington Rand corporation in October 1953. It was the first computer for which Seymour Cray was credited with design work.
History
Even before the completion of the Atlas (UNIVAC 1101), the Navy asked Engineering Research Associates to design a more powerful machine. This project became Task 29, and the computer was designated Atlas II.
In 1952, Engineering Research Associates asked the Armed Forces Security Agency (the predecessor of the NSA) for approval to sell the Atlas II commercially. Permission was given, on the condition that several specialized instructions would be removed. The commercial version then became the UNIVAC 1103. Because of security classification, Remington Rand management was unaware of this machine before this. The first commercially sold UNIVAC 1103 was sold to the aircraft manufacturer Convair, where Marvin Stein worked with it.
Remington Rand announced the UNIVAC 1103 in February 1953. The machine competed with the IBM 701 in the scientific computation market. In early 1954, a committee of the Joint Chiefs of Staff requested that the two machines be compared for the purpose of using them for a Joint Numerical Weather Prediction project. Based on the trials, the two machines had comparable computational speed, with a slight advantage for IBM's machine, but the latter was favored unanimously for its significantly faster input-output equipment.
The successor machine was the UNIVAC 1103A or Univac Scientific, which improved upon the design by replacing the unreliable Williams tube memory with magnetic-core memory, adding hardware floating-point instructions, and perhaps the earliest occurrence of a hardware interrupt feature.
Technical details
The system used electrostatic storage, consisting of 36 Williams tubes with a capacity of 1024 bits each, giving a total random-access memory of 1024 word |
https://en.wikipedia.org/wiki/Trapdoor%20function | In theoretical computer science and cryptography, a trapdoor function is a function that is easy to compute in one direction, yet difficult to compute in the opposite direction (finding its inverse) without special information, called the "trapdoor". Trapdoor functions are a special case of one-way functions and are widely used in public-key cryptography.
In mathematical terms, if f is a trapdoor function, then there exists some secret information t, such that given f(x) and t, it is easy to compute x. Consider a padlock and its key. It is trivial to change the padlock from open to closed without using the key, by pushing the shackle into the lock mechanism. Opening the padlock easily, however, requires the key to be used. Here the key t is the trapdoor and the padlock is the trapdoor function.
An example of a simple mathematical trapdoor is "6895601 is the product of two prime numbers. What are those numbers?" A typical "brute-force" solution would be to try dividing 6895601 by many prime numbers until finding the answer. However, if one is told that 1931 is one of the numbers, one can find the answer by entering "6895601 ÷ 1931" into any calculator. This example is not a sturdy trapdoor function – modern computers can guess all of the possible answers within a second – but this sample problem could be improved by using the product of two much larger primes.
Trapdoor functions came to prominence in cryptography in the mid-1970s with the publication of asymmetric (or public-key) encryption techniques by Diffie, Hellman, and Merkle. Indeed, coined the term. Several function classes had been proposed, and it soon became obvious that trapdoor functions are harder to find than was initially thought. For example, an early suggestion was to use schemes based on the subset sum problem. This turned out rather quickly to be unsuitable.
, the best known trapdoor function (family) candidates are the RSA and Rabin families of functions. Both are written as exponentiation |
https://en.wikipedia.org/wiki/IBM%20709 | The IBM 709 was a computer system, initially announced by IBM in January 1957 and first installed during August 1958. The 709 was an improved version of its predecessor, the IBM 704, and was the third of the IBM 700/7000 series of scientific computers. The improvements included overlapped input/output, indirect addressing, and three "convert" instructions which provided support for decimal arithmetic, leading zero suppression, and several other operations. The 709 had 32,768 words of 36-bit magnetic core memory and could execute 42,000 add or subtract instructions per second. It could multiply two 36-bit integers at a rate of 5000 per second.
An optional hardware emulator executed old IBM 704 programs on the IBM 709. This was the first commercially available emulator. Registers and most 704 instructions were emulated in 709 hardware. Complex 704 instructions such as floating point trap and input-output routines were emulated in 709 software.
The FORTRAN Assembly Program was first introduced for the 709.
It was a large system; customer installations used 100 to 250 kW to run them and almost as much again on the cooling. It weighed about (without peripheral equipment).
The 709 was built using vacuum tubes.
IBM announced a transistorized version of the 709, called the IBM 7090, in 1958, only a year after the announcement of the 709, thus cutting short the 709's product life.
Registers
The IBM 709 has a 38-bit accumulator, a 36-bit multiplier quotient register, and three 15-bit index registers whose contents are subtracted from the base address instead of being added to it. All three index registers can participate in an instruction: the 3-bit tag field in the instruction is a bit map specifying which of the registers participate in the operation, however if more than one index register is specified, their contents are combined by a logical or operation, not addition.p. 12
Instruction and data formats
There are five instruction formats, referred to as Types A, |
https://en.wikipedia.org/wiki/Operating%20environment | In computer software, an operating environment or integrated applications environment is the environment in which users run application software. The environment consists of a user interface provided by an applications manager and usually an application programming interface (API) to the applications manager.
An operating environment is not a full operating system, but is a form of middleware that rests between the OS and the application. For example, the first version of Microsoft Windows, Windows 1.0, was not a full operating system, but a GUI laid over DOS albeit with an API of its own. Similarly, the IBM U2 system operates on both Unix/Linux and Windows NT. Usually the user interface is text-based or graphical, rather than a command-line interface (e.g., DOS or the Unix shell), which is often the interface of the underlying operating system.
In the mid 1980s, text-based and graphical user interface operating environments surrounded DOS operating systems with a shell that turned the user's display into a menu-oriented "desktop" for selecting and running PC applications. These operating environment systems allow users much of the convenience of integrated software without locking them into a single package.
History
DOS operating environments
In the mid 1980s, text-based and graphical user interface operating environments such as IBM TopView, Microsoft Windows, Digital Research's GEM Desktop, GEOS and Quarterdeck Office Systems's DESQview surrounded DOS operating systems with a shell that turned the user's display into a menu-oriented "desktop" for selecting and running PC applications. These programs were more than simple menu systems—as alternate operating environments they were substitutes for integrated programs such as Framework and Symphony, that allowed switching, windowing, and cut-and-paste operations among dedicated applications. These operating environment systems gave users much of the convenience of integrated software without locking them into |
https://en.wikipedia.org/wiki/Program%20evaluation%20and%20review%20technique | The program evaluation and review technique (PERT) is a statistical tool used in project management, which was designed to analyze and represent the tasks involved in completing a given project.
First developed by the United States Navy in 1958, it is commonly used in conjunction with the critical path method (CPM) that was introduced in 1957.
Overview
PERT is a method of analyzing the tasks involved in completing a given project, especially the time needed to complete each task, and to identify the minimum time needed to complete the total project. It incorporates uncertainty by making it possible to schedule a project while not knowing precisely the details and durations of all the activities. It is more of an event-oriented technique rather than start- and completion-oriented, and is used more in those projects where time is the major factor rather than cost. It is applied on very large-scale, one-time, complex, non-routine infrastructure and on Research and Development projects.
PERT offers a management tool, which relies "on arrow and node diagrams of activities and events: arrows represent the activities or work necessary to reach the events or nodes that indicate each completed phase of the total project."
PERT and CPM are complementary tools, because "CPM employs one time estimation and one cost estimation for each activity; PERT may utilize three time estimates (optimistic, expected, and pessimistic) and no costs for each activity. Although these are distinct differences, the term PERT is applied increasingly to all critical path scheduling."
History
PERT was developed primarily to simplify the planning and scheduling of large and complex projects. It was developed for the U.S. Navy Special Projects Office in 1957 to support the U.S. Navy's Polaris nuclear submarine project. It found applications all over industry. An early example is when it was used for the 1968 Winter Olympics in Grenoble which applied PERT from 1965 until the opening of the 1968 |
https://en.wikipedia.org/wiki/Beta%20distribution | In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1) in terms of two positive parameters, denoted by alpha (α) and beta (β), that appear as exponents of the variable and its complement to 1, respectively, and control the shape of the distribution.
The beta distribution has been applied to model the behavior of random variables limited to intervals of finite length in a wide variety of disciplines. The beta distribution is a suitable model for the random behavior of percentages and proportions.
In Bayesian inference, the beta distribution is the conjugate prior probability distribution for the Bernoulli, binomial, negative binomial and geometric distributions.
The formulation of the beta distribution discussed here is also known as the beta distribution of the first kind, whereas beta distribution of the second kind is an alternative name for the beta prime distribution. The generalization to multiple variables is called a Dirichlet distribution.
Definitions
Probability density function
The probability density function (PDF) of the beta distribution, for or , and shape parameters α, β > 0, is a power function of the variable x and of its reflection as follows:
where Γ(z) is the gamma function. The beta function, , is a normalization constant to ensure that the total probability is 1. In the above equations x is a realization—an observed value that actually occurred—of a random variable X.
Several authors, including N. L. Johnson and S. Kotz, use the symbols p and q (instead of α and β) for the shape parameters of the beta distribution, reminiscent of the symbols traditionally used for the parameters of the Bernoulli distribution, because the beta distribution approaches the Bernoulli distribution in the limit when both shape parameters α and β approach the value of zero.
In the following, a random variable X beta-distributed with parameters α and β will |
https://en.wikipedia.org/wiki/Gamma%20distribution | In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution, and chi-squared distribution are special cases of the gamma distribution. There are two equivalent parameterizations in common use:
With a shape parameter and a scale parameter .
With a shape parameter and an inverse scale parameter , called a rate parameter.
In each of these forms, both parameters are positive real numbers.
The gamma distribution is the maximum entropy probability distribution (both with respect to a uniform base measure and a base measure) for a random variable for which E[X] = kθ = α/β is fixed and greater than zero, and E[ln(X)] = ψ(k) + ln(θ) = ψ(α) − ln(β) is fixed (ψ is the digamma function).
Definitions
The parameterization with k and θ appears to be more common in econometrics and other applied fields, where the gamma distribution is frequently used to model waiting times. For instance, in life testing, the waiting time until death is a random variable that is frequently modeled with a gamma distribution. See Hogg and Craig for an explicit motivation.
The parameterization with and is more common in Bayesian statistics, where the gamma distribution is used as a conjugate prior distribution for various types of inverse scale (rate) parameters, such as the λ of an exponential distribution or a Poisson distribution – or for that matter, the β of the gamma distribution itself. The closely related inverse-gamma distribution is used as a conjugate prior for scale parameters, such as the variance of a normal distribution.
If k is a positive integer, then the distribution represents an Erlang distribution; i.e., the sum of k independent exponentially distributed random variables, each of which has a mean of θ.
Characterization using shape α and rate β
The gamma distribution can be parameterized in terms of a shape parameter α = k and an inverse scale parame |
https://en.wikipedia.org/wiki/Triangulation | In trigonometry and geometry, triangulation is the process of determining the location of a point by forming triangles to the point from known points.
Applications
In surveying
Specifically in surveying, triangulation involves only angle measurements at known points, rather than measuring distances to the point directly as in trilateration; the use of both angles and distance measurements is referred to as triangulateration.
In computer vision
Computer stereo vision and optical 3D measuring systems use this principle to determine the spatial dimensions and the geometry of an item. Basically, the configuration consists of two sensors observing the item. One of the sensors is typically a digital camera device, and the other one can also be a camera or a light projector. The projection centers of the sensors and the considered point on the object's surface define a (spatial) triangle. Within this triangle, the distance between the sensors is the base b and must be known. By determining the angles between the projection rays of the sensors and the basis, the intersection point, and thus the 3D coordinate, is calculated from the triangular relations.
History
Triangulation today is used for many purposes, including surveying, navigation, metrology, astrometry, binocular vision, model rocketry and, in the military, the gun direction, the trajectory and distribution of fire power of weapons.
The use of triangles to estimate distances dates to antiquity. In the 6th century BC, about 250 years prior to the establishment of the Ptolemaic dynasty, the Greek philosopher Thales is recorded as using similar triangles to estimate the height of the pyramids of ancient Egypt. He measured the length of the pyramids' shadows and that of his own at the same moment, and compared the ratios to his height (intercept theorem). Thales also estimated the distances to ships at sea as seen from a clifftop by measuring the horizontal distance traversed by the line-of-sight for a known fa |
https://en.wikipedia.org/wiki/Combinatorial%20search | In computer science and artificial intelligence, combinatorial search studies search algorithms for solving instances of problems that are believed to be hard in general, by efficiently exploring the usually large solution space of these instances. Combinatorial search algorithms achieve this efficiency by reducing the effective size of the search space or employing heuristics. Some algorithms are guaranteed to find the optimal solution, while others may only return the best solution found in the part of the state space that was explored.
Classic combinatorial search problems include solving the eight queens puzzle or evaluating moves in games with a large game tree, such as reversi or chess.
A study of computational complexity theory helps to motivate combinatorial search. Combinatorial search algorithms are typically concerned with problems that are NP-hard. Such problems are not believed to be efficiently solvable in general. However, the various approximations of complexity theory suggest that some instances (e.g. "small" instances) of these problems could be efficiently solved. This is indeed the case, and such instances often have important practical ramifications.
Examples
Common algorithms for solving combinatorial search problems include:
A* search algorithm
Alpha–beta pruning
Branch-and-bound
Minimax
Lookahead
Lookahead is an important component of combinatorial search, which specifies, roughly, how deeply the graph representing the problem is explored. The need for a specific limit on lookahead comes from the large problem graphs in many applications, such as computer chess and computer Go. A naive breadth-first search of these graphs would quickly consume all the memory of any modern computer. By setting a specific lookahead limit, the algorithm's time can be carefully controlled; its time increases exponentially as the lookahead limit increases.
More sophisticated search techniques such as alpha–beta pruning are able to eliminate entire s |
https://en.wikipedia.org/wiki/Moritz%20Schlick | Friedrich Albert Moritz Schlick (; ; 14 April 1882 – 22 June 1936) was a German philosopher, physicist, and the founding father of logical positivism and the Vienna Circle.
Early life and works
Schlick was born in Berlin to a wealthy Prussian family with deep nationalist and conservative traditions. His father was Ernst Albert Schlick and his mother was Agnes Arndt. At the age of sixteen, he started to read Descartes' Meditations and Schopenhauer's Die beiden Grundprobleme der Ethik. Nietzsche's Also sprach Zarathustra especially impressed him.
He studied physics at the University of Heidelberg, the University of Lausanne, and, ultimately, the University of Berlin under Max Planck. Schlick explained this choice in his autobiography by saying that, despite his love for philosophy, he believed that only mathematical physics could help him obtain actual and exact knowledge. He felt deep distrust towards any metaphysical speculation.
In 1904, he completed his PhD thesis at the University of Berlin under the supervision of Planck. Schlick's thesis was titled Über die Reflexion des Lichts in einer inhomogenen Schicht (On the Reflection of Light in a Non-Homogeneous Medium). After a year as Privatdozent at Göttingen, he turned to the study of philosophy in Zurich. In 1907, he married Blanche Hardy. In 1908, he published Lebensweisheit (The Wisdom of Life), a slim volume about eudaemonism, the theory that happiness results from the pursuit of personal fulfillment as opposed to passing pleasures.
His habilitation thesis at the University of Rostock, Das Wesen der Wahrheit nach der modernen Logik (The Nature of Truth According to Modern Logic), was published in 1910. Several essays about aesthetics followed, whereupon Schlick turned his attention to problems of epistemology, the philosophy of science, and more general questions about science. In this last category, Schlick distinguished himself by publishing a paper in 1915 about Einstein's special theory of relativity, a |
https://en.wikipedia.org/wiki/Put%E2%80%93call%20parity | In financial mathematics, the put–call parity defines a relationship between the price of a European call option and European put option, both with the identical strike price and expiry, namely that a portfolio of a long call option and a short put option is equivalent to (and hence has the same value as) a single forward contract at this strike price and expiry. This is because if the price at expiry is above the strike price, the call will be exercised, while if it is below, the put will be exercised, and thus in either case one unit of the asset will be purchased for the strike price, exactly as in a forward contract.
The validity of this relationship requires that certain assumptions be satisfied; these are specified and the relationship is derived below. In practice transaction costs and financing costs (leverage) mean this relationship will not exactly hold, but in liquid markets the relationship is close to exact.
Assumptions
Put–call parity is a static replication, and thus requires minimal assumptions, namely the existence of a forward contract. In the absence of traded forward contracts, the forward contract can be replaced (indeed, itself replicated) by the ability to buy the underlying asset and finance this by borrowing for fixed term (e.g., borrowing bonds), or conversely to borrow and sell (short) the underlying asset and loan the received money for term, in both cases yielding a self-financing portfolio.
These assumptions do not require any transactions between the initial date and expiry, and are thus significantly weaker than those of the Black–Scholes model, which requires dynamic replication and continual transaction in the underlying.
Replication assumes one can enter into derivative transactions, which requires leverage (and capital costs to back this), and buying and selling entails transaction costs, notably the bid–ask spread. The relationship thus only holds exactly in an ideal frictionless market with unlimited liquidity. However, real |
https://en.wikipedia.org/wiki/Medium%20access%20control | In IEEE 802 LAN/MAN standards, the medium access control (MAC), also called media access control, is the layer that controls the hardware responsible for interaction with the wired (electrical or optical) or wireless transmission medium. The MAC sublayer and the logical link control (LLC) sublayer together make up the data link layer. The LLC provides flow control and multiplexing for the logical link (i.e. EtherType, 802.1Q VLAN tag etc), while the MAC provides flow control and multiplexing for the transmission medium.
These two sublayers together correspond to layer 2 of the OSI model. For compatibility reasons, LLC is optional for implementations of IEEE 802.3 (the frames are then "raw"), but compulsory for implementations of other IEEE 802 physical layer standards. Within the hierarchy of the OSI model and IEEE 802 standards, the MAC sublayer provides a control abstraction of the physical layer such that the complexities of physical link control are invisible to the LLC and upper layers of the network stack. Thus any LLC sublayer (and higher layers) may be used with any MAC. In turn, the medium access control block is formally connected to the PHY via a media-independent interface. Although the MAC block is today typically integrated with the PHY within the same device package, historically any MAC could be used with any PHY, independent of the transmission medium.
When sending data to another device on the network, the MAC sublayer encapsulates higher-level frames into frames appropriate for the transmission medium (i.e. the MAC adds a syncword preamble and also padding if necessary), adds a frame check sequence to identify transmission errors, and then forwards the data to the physical layer as soon as the appropriate channel access method permits it. For topologies with a collision domain (bus, ring, mesh, point-to-multipoint topologies), controlling when data is sent and when to wait is necessary to avoid collisions. Additionally, the MAC is also responsib |
https://en.wikipedia.org/wiki/Processor%20power%20dissipation | Processor power dissipation or processing unit power dissipation is the process in which computer processors consume electrical energy, and dissipate this energy in the form of heat due to the resistance in the electronic circuits.
Power management
Designing CPUs that perform tasks efficiently without overheating is a major consideration of nearly all CPU manufacturers to date. Historically, early CPUs implemented with vacuum tubes consumed power on the order of many kilowatts. Current CPUs in general-purpose personal computers, such as desktops and laptops, consume power in the order of tens to hundreds of watts. Some other CPU implementations use very little power; for example, the CPUs in mobile phones often use just a few watts of electricity, while some microcontrollers used in embedded systems may consume only a few milliwatts or even as little as a few microwatts.
There are a number of engineering reasons for this pattern:
For a given CPU core, energy usage will scale up as its clock rate increases. Reducing the clock rate or undervolting usually reduces energy consumption; it is also possible to undervolt the microprocessor while keeping the clock rate the same.
New features generally require more transistors, each of which uses power. Turning unused areas off saves energy, such as through clock gating.
As a processor model's design matures, smaller transistors, lower-voltage structures, and design experience may reduce energy consumption.
Processor manufacturers usually release two power consumption numbers for a CPU:
typical thermal power, which is measured under normal load (for instance, AMD's average CPU power)
maximum thermal power, which is measured under a worst-case load
For example, the Pentium 4 2.8 GHz has a 68.4 W typical thermal power and 85 W maximum thermal power. When the CPU is idle, it will draw far less than the typical thermal power. Datasheets normally contain the thermal design power (TDP), which is the maximum amount of hea |
https://en.wikipedia.org/wiki/VIA%20Technologies | VIA Technologies Inc. (), is a Taiwanese manufacturer of integrated circuits, mainly motherboard chipsets, CPUs, and memory. It was the world's largest independent manufacturer of motherboard chipsets. As a fabless semiconductor company, VIA conducts research and development of its chipsets in-house, then subcontracts the actual (silicon) manufacturing to third-party merchant foundries such as TSMC.
VIA is also the parent company of VIA Labs Inc. (VLI, ). As an independently-traded subsidiary, VLI develops and markets USB 3, USB 4, USB Type-C, and USB PD controllers for computer peripherals and mobile devices.
History
The company was founded in 1987, in Fremont, California, USA by Cher Wang. In 1992, it was decided to move the headquarters to Taipei, Taiwan in order to establish closer partnerships with the substantial and growing IT manufacturing base in Taiwan and neighbouring China.
In 1999, VIA acquired most of Cyrix, then a division of National Semiconductor. That same year, VIA acquired Centaur Technology from Integrated Device Technology, marking its entry into the x86 microprocessor market. VIA is the maker of the VIA C3, VIA C7 & VIA Nano processors, and the EPIA platform. The Cyrix MediaGX platform remained with National Semiconductor.
In 2001, VIA established the S3 Graphics joint venture.
In January 2005, VIA began the VIA pc-1 Initiative, to develop information and communication technology systems to benefit those with no access to computers or Internet. In February 2005, VIA celebrated production of the 100 millionth VIA AMD chipset.
On 29 August 2008, VIA announced that they would release official 2D accelerated Linux drivers for their chipsets, and would also release 3D accelerated drivers.
In July 2008, VIA Labs, Inc. (VLI) was founded as a wholly-owned subsidiary of VIA Technologies Inc. (VIA) to develop and market integrated circuits primarily for USB 3.0. VLI was intended to be a "smaller and thus more agile" company that can quickly respo |
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