Language identification in the limit is a formal model for inductive inference of formal languages, mainly by computers (see machine learning and induction of regular languages). It was introduced by E. Mark Gold in a technical report and a journal article with the same title. In this model, a teacher provides to a learner some presentation (i.e. a sequence of strings) of some formal language. The learning is seen as an infinite process. Each time the learner reads an element of the presentation, it should provide a representation (e.g. a formal grammar) for the language. Gold defines that a learner can identify in the limit a class of languages if, given any presentation of any language in the class, the learner will produce only a finite number of wrong representations, and then stick with the correct representation. However, the learner need not be able to announce its correctness; and the teacher might present a counterexample to any representation arbitrarily long after. Gold defined two types of presentations: Text (positive information): an enumeration of all strings the language consists of. Complete presentation (positive and negative information): an enumeration of all possible strings, each with a label indicating if the string belongs to the language or not. == Learnability == This model is an early attempt to formally capture the notion of learnability. Gold's journal article introduces for contrast the stronger models Finite identification (where the learner has to announce correctness after a finite number of steps), and Fixed-time identification (where correctness has to be reached after an apriori-specified number of steps). A weaker formal model of learnability is the Probably approximately correct learning (PAC) model, introduced by Leslie Valiant in 1984. == Examples == It is instructive to look at concrete examples (in the tables) of learning sessions the definition of identification in the limit speaks about. A fictitious session to learn a regular language L over the alphabet {a,b} from text presentation:In each step, the teacher gives a string belonging to L, and the learner answers a guess for L, encoded as a regular expression. In step 3, the learner's guess is not consistent with the strings seen so far; in step 4, the teacher gives a string repeatedly. After step 6, the learner sticks to the regular expression (ab+ba). If this happens to be a description of the language L the teacher has in mind, it is said that the learner has learned that language.If a computer program for the learner's role would exist that was able to successfully learn each regular language, that class of languages would be identifiable in the limit. Gold has shown that this is not the case. A particular learning algorithm always guessing L to be just the union of all strings seen so far:If L is a finite language, the learner will eventually guess it correctly, however, without being able to tell when. Although the guess didn't change during step 3 to 6, the learner couldn't be sure to be correct.Gold has shown that the class of finite languages is identifiable in the limit, however, this class is neither finitely nor fixed-time identifiable. Learning from complete presentation by telling:In each step, the teacher gives a string and tells whether it belongs to L (green) or not (red, struck-out). Each possible string is eventually classified in this way by the teacher. Learning from complete presentation by request:The learner gives a query string, the teacher tells whether it belongs to L (yes) or not (no); the learner then gives a guess for L, followed by the next query string. In this example, the learner happens to query in each step just the same string as given by the teacher in example 3.In general, Gold has shown that each language class identifiable in the request-presentation setting is also identifiable in the telling-presentation setting, since the learner, instead of querying a string, just needs to wait until it is eventually given by the teacher. == Gold's theorem == More formally, a language L {\displaystyle L} is a nonempty set, and its elements are called sentences. a language family is a set of languages. a language-learning environment E {\displaystyle E} for a language L {\displaystyle L} is a stream of sentences from L {\displaystyle L} , such that each sentence in L {\displaystyle L} appears at least once. a language learner is a function f {\displaystyle f} that sends a list of sentences to a language. This is interpreted as saying that, after seeing sentences a 1 , a 2 . . . , a n {\displaystyle a_{1},a_{2}...,a_{n}} in that order, the language learner guesses that the language that produces the sentences should be f ( a 1 , . . . , a n ) {\displaystyle f(a_{1},...,a_{n})} . Note that the learner is not obliged to be correct — it could very well guess a language that does not even contain a 1 , . . . , a n {\displaystyle a_{1},...,a_{n}} . a language learner f {\displaystyle f} learns a language L {\displaystyle L} in environment E = ( a 1 , a 2 , . . . ) {\displaystyle E=(a_{1},a_{2},...)} if the learner always guesses L {\displaystyle L} after seeing enough examples from the environment. a language learner f {\displaystyle f} learns a language L {\displaystyle L} if it learns L {\displaystyle L} in any environment E {\displaystyle E} for L {\displaystyle L} . a language family is learnable if there exists a language learner that can learn all languages in the family. Notes: In the context of Gold's theorem, sentences need only be distinguishable. They need not be anything in particular, such as finite strings (as usual in formal linguistics). Learnability is not a concept for individual languages. Any individual language L {\displaystyle L} could be learned by a trivial learner that always guesses L {\displaystyle L} . Learnability is not a concept for individual learners. A language family is learnable if, and only if, there exists some learner that can learn the family. It does not matter how well the learner performs for learning languages outside the family. Gold's theorem is easily bypassed if negative examples are allowed. In particular, the language family { L 1 , L 2 , . . . , L ∞ } {\displaystyle \{L_{1},L_{2},...,L_{\infty }\}} can be learned by a learner that always guesses L ∞ {\displaystyle L_{\infty }} until it receives the first negative example ¬ a n {\displaystyle \neg a_{n}} , where a n ∈ L n + 1 ∖ L n {\displaystyle a_{n}\in L_{n+1}\setminus L_{n}} , at which point it always guesses L n {\displaystyle L_{n}} . == Learnability characterization == Dana Angluin gave the characterizations of learnability from text (positive information) in a 1980 paper. If a learner is required to be effective, then an indexed class of recursive languages is learnable in the limit if there is an effective procedure that uniformly enumerates tell-tales for each language in the class (Condition 1). It is not hard to see that if an ideal learner (i.e., an arbitrary function) is allowed, then an indexed class of languages is learnable in the limit if each language in the class has a tell-tale (Condition 2). == Language classes learnable in the limit == The table shows which language classes are identifiable in the limit in which learning model. On the right-hand side, each language class is a superclass of all lower classes. Each learning model (i.e. type of presentation) can identify in the limit all classes below it. In particular, the class of finite languages is identifiable in the limit by text presentation (cf. Example 2 above), while the class of regular languages is not. Pattern Languages, introduced by Dana Angluin in another 1980 paper, are also identifiable by normal text presentation; they are omitted in the table, since they are above the singleton and below the primitive recursive language class, but incomparable to the classes in between. == Sufficient conditions for learnability == Condition 1 in Angluin's paper is not always easy to verify. Therefore, people come up with various sufficient conditions for the learnability of a language class. See also Induction of regular languages for learnable subclasses of regular languages. === Finite thickness === A class of languages has finite thickness if every non-empty set of strings is contained in at most finitely many languages of the class. This is exactly Condition 3 in Angluin's paper. Angluin showed that if a class of recursive languages has finite thickness, then it is learnable in the limit. A class with finite thickness certainly satisfies MEF-condition and MFF-condition; in other words, finite thickness implies M-finite thickness. === Finite elasticity === A class of languages is said to have finite elasticity if for every infinite sequence of strings s 0 , s 1 , . . . {\displaystyle s_{0},s_{1},...} and every infinite sequence of languages in the class L 1 , L 2 , . . . {\displaystyle L_{1},L_{2},...} , there exists a finite number n such
Magisto
Magisto provided an online video editing tool (both as a web application and a mobile app) for automated video editing and production. In 2019, the company was acquired by Vimeo for an estimated US$200 million. The Magisto app contained a library of music. The music, largely by independent artists, was sorted by mood and is licensed for in-app use. Magisto had a freemium business model where users can create basic video clips for free. In addition, advanced business, professional and personal service tiers are available via various subscription plans, unlocking more features; such as longer videos, HD, premium themes, customization, and control features. == History == Magisto was founded in 2009 as SightEra (LTD) by Oren Boiman (CEO) and Alex Rav-Acha (CTO). Boiman, frustrated with the amount of time it took editing together videos of his daughter, wanted an easier to use application to capture and share videos. Boiman, a computer scientist that graduated from Tel Aviv University, followed with graduate work in computer vision at the Weizmann Institute of Science. Boiman developed several patent-pending image analysis technologies that analyze unedited videos to identify the most interesting parts. The system recognized faces, animals, landscapes, action sequences, movements and other important content within the video, as well as analyzing speech and audio. These scenes are then edited together, along with music and effects. Magisto was launched publicly on September 20, 2011, as a video editing software web application through which users could upload unedited video footage, choose a title and soundtrack and have their video edited for them automatically. On the following day, Magisto was added to YouTube Create's collection of video production applications. The Magisto iPhone app was launched publicly at the 2012 International Consumer Electronics Show (CES) in Las Vegas. At CES, the company was also awarded first place in the 2012 CES Mobile App Showdown. In August 2012, Magisto launched the Android app on Google Play. In September 2012, Magisto launched a Google Chrome App and announced Google Drive integration. In March 2013, Magisto claimed it had 5 million users. Google listed Magisto as an "Editors’ Choice" on its list of "Best Apps of 2013". In September 2013, the company claimed that 10 million users had downloaded the App. In February 2014, Magisto claimed that they had 20 million users, with 2 million new users per month. The company also confirmed investment from Mail.Ru. In September 2014, Magisto rolled out a feature called 'Instagram Ready' which allowed users to upload 15 second clips that are automatically formatted for Instagram. In the same month, Magisto launched a feature for iOS and Android users, called 'Surprise Me', which created video from still photography on users’ smartphones. In October 2014, Magisto was placed 9th on the 2014 Deloitte Israel Technology Fast 50 list and named as a finalist in the Red Herring's Top 100 Europe award. In July 2015, Magisto released an editing theme dedicated to Jerry Garcia. In April 2019, the company was acquired by Vimeo, the IAC-owned platform for hosting, sharing and monetizing streamed video, for an estimated $200 million. === Financing === In 2011, the company received more than $5.5 million in a Series B venture round funding from Magma Venture Partners and Horizons Ventures. In September 2011, at the same time as the public launch of their web application, Magisto announced a $5.5 million Series B funding round led by Li Ka-shing’s Horizons Ventures. Li Ka-Shing is known for making early-stage investments in companies like Facebook, Spotify, SecondMarket and Siri. In October 2013, the company received $13 million in funding from Qualcomm and Sandisk. In 2014, the company received $2 million in Venture Funding from Magma Venture Partners, Qualcomm Ventures, Horizons Ventures and the Mail.Ru Group. == Awards == Magisto won first place at Technonomy3, an annual Internet Technology start-up competition in Israel. Judges of the competition included Jeff Pulver, TechCrunch editor Mike Butcher, investor Yaron Samid, Bessemer Venture Partners Israel partner Adam Fisher and Brad McCarty of The Next Web. Magisto won first place at CES 2012 Mobile app competition, during the launch of Magisto iOS mobile app. Magisto was awarded twice the Google Play Editor's Choice and was part of iPhone App Store Best App awards for 2013 and 2014, and Wired Essential iPad Apps. Magisto was declared by Deloitte as the 7th fastest growing company in Europe, the Middle East, and Africa in 2016.
Fan loyalty
Fan loyalty is the loyalty felt and expressed by a fan towards the object of their fanaticism. Fan loyalty is often used in the context of sports and the support of a specific team or institution. Fan loyalties can range from a passive support to radical allegiance and expressions of loyalty can take shape in many forms and be displayed across varying platforms. Fan loyalty can be threatened by team actions. The loyalties of sports fans in particular have been studied by psychologists, who have determined several factors that help to create such loyalties. == Underpinning psychology == Given the extensive costs involved in managing and operating a professional team sport, it is beneficial for sports marketers to be conscious of the elements that establish a strong brand and the effect they have on fan loyalty, so they can best cater to their current fans while acquiring new ones. This is because fans and spectators are considered key stakeholders of professional sports organisations. Fans directly and indirectly influence the production of operating revenue through purchasing merchandise, buying game tickets and improving the value that can be obtained from television broadcasting deals and sponsorship. Therefore, fans are a key factor to consider in determining the economic success of a sports club. Deep psychological connections with new teams can be built with individuals before a team has even played a match revealing insights can develop quickly in the mind of consumers without direct encounters or experiences e.g. watching a team compete. Brand management approaches are helping sport organisations to expand the sport experience, appeal to new fans and enable long term business to consumer relationships through multi faceted connection such as social media. To affect consumers’ loyalty with a team, they must develop a compelling, positive and distinctive brand in order to stand out amongst competitor and vie for fan support. Loyalty programmes positively shape fan attachment and behaviour as it connects teams and their fans, aside from a club's season ticketholder database. It not only provides marketers with essential information about consumers and their thinking, but also acts as a channel to promote attendance and an opportunity to add value to their game day experience. Bauer et al. concludes that non product related attributes such as contextual factors (other fans, the club history and tradition, logo, club colours and the stadium atmosphere) hold a higher place in fan experience than product related attributes such as the team's winning record. Therefore, to increase fan loyalty (customer retention) Bauer et al. suggests sports marketers focus on targeting non product related benefits and brand attributes. As a result of fostering this loyalty, sports organisations can afford to charge prices at premium. Fan loyalty also leads to dependable ratings in broadcast media which means broadcasters can also charge premiums for advertising time in team broadcasts with loyal followings. A flow on effect from fan loyalty is the ability to create additional revenue streams outside of the core product such as merchandise shops and food venues that are close to the location of the game if the team chooses to own and operate ventures or share licensing agreements. Fan loyalty, particularly with respect to team sports, is different from brand loyalty, in as much as if a consumer bought a product that was of lower quality than expected, he or she will usually abandon allegiance to the brand. However, fan loyalty continues even if the team that the fan supports continues to perform poorly year after year. Author Mark Conrad uses the Chicago Cubs as an example of a team with a loyal fan following, where fans spend their money in support of a poorly performing team that (until 2016) had not won a pennant since 1945 or a World Series since 1908. They attribute it to the following factors: Entertainment Value The entertainment value that a fan derives from spectating motivates him/her to remain a loyal fan. Entertainment value of team sports is also valuable to communities in general. Authenticity This is described by Passikoff as "the acceptance of the game as real and meaningful". Fan Bonding Fan bonding is where a fan bonds with the players, identifying with them as individuals, and bonds with the team. Team History and Tradition Shank gives the Cincinnati Reds, all-professional baseball's oldest team, as an example of a team where a long team history and tradition is a motivator for fans in the Cincinnati area. Group Affiliation Fans receive personal validation of their support for a team from being surrounded by a group of fans who also support the same team. Fair Weather Fans Fans that engage when a team is good, and lose interest when a team is bad. Bandwagon Fans Fans who support the winning team, instead of supporting the same team year after year. Diehard Fans Fans who follow their team no matter if they are winning or losing. == Factors influencing fan loyalty == === Community === Fan loyalty attachment is strengthened through communal ties that connect fans around a team, forming a community that results in regular fan interaction. This interaction is particularly important as fans may not develop solely an intra-psychic team identity but predominantly display behavioural loyalty through the group consumption of indirect sport experiences instead, such as wearing the team colours, singing, cheering, flags and interaction between the sport's team's fans (e.g. laughing, talking) Through indirect sport experiences, the stadium atmosphere can be heightened and as a result, the frequency of fan attendance can increase. Furthermore, by wearing team apparel, fans can visually identify with one another resulting an increased likelihood of opportunities to engage with others socially through this point of connection. For example, a study on NASCAR fans found that their personal identity was connected to the brand itself as they felt connected to the larger community of NASCAR revealing an emotional connection to the brand. This indicates that their fan loyalty will result in the notion that fans are naturally more resistant to the promotional efforts of competing brands (e.g. lower-price offers) as their emotional commitment to NASCAR is greatly embedded in their sense of identity. When they associate themselves with the sponsors because of the sponsor's relation to the brand, they are solidifying their relationship with NASCAR and are therefore reinforcing their identity. Consequently, their fan loyalty translates into brand loyalty so long as the sponsor remains attached to the subject of their fanaticism, NASCAR, meaning they are less price sensitive and more willing to pay premium prices for sponsor's products or services. Another aspect of consumer behaviour regarding fan loyalty is the existence of consumption communities where members feel a sense of unity when they participate in the group consumption of brand sponsors’ goods and services further strengthening their ties to a brand and its sponsors. However, a strategy sports marketers use to appeal to a wider range of fan identities is to sponsor more than one club in sports such as soccer. This is so they are careful not to come across as a singularly affiliated club brand, where the opinion or perceptions of opposing teams’ fans would be one of disfavour towards them. === Brand association === Any benefit or characteristic connected to a brand as perceived by a consumer is called a brand association. These hold significance over the thoughts and opinions a consumer holds about a brand and can therefore influence one's loyalty. These associations provide a reference point to gauge the salience of a brand which is the perceived favourability associated with it. Brand salience is vital because it ultimately effects the likelihood of brand selection and loyalty leading to steadier spectator numbers, and an increase in attention from the media such as advertisers and sponsors. However, loyalty is a developmental process. According to Bee & Havitz (2010), spectators who are highly involved in the participation of a sport and exhibit psychological commitment, possess the capability to display high levels of behavioural loyalty as they develop into committed fans. On the other hand, neutral or negative feelings towards a team are found to foster indifference or cause an individual to disidentify with a team altogether. A model of ‘escalating commitment’, put forward by Funk and James (2001), demonstrates an individual's movement from ‘awareness’ of team to a subsequent ‘allegiance’ but came to the conclusion that more research was required to find out the key influences that lead one to the highest state of commitment. However, brand association development is fostered under brand management within a sports organisation. It is important for sports management research to identify t
Digital cassettes
Digital audio cassette formats introduced to the professional audio and consumer markets: Digital Audio Tape (or DAT) is the most well-known, and had some success as an audio storage format among professionals and "prosumers" before the prices of hard drive and solid-state flash memory-based digital recording devices dropped in the late 1990s. Hard-drive recording has mostly made DAT obsolete, as hard disk recorders offer more editing versatility than tape, and easier importation into digital audio workstations (DAWs) and non-linear video editing (NLE) systems. Digital Compact Cassette was intended as a digital replacement for the mass-market analog cassette tape, but received very little attention or adaptation. Its failure is generally attributed to higher production costs than audio CDs, durability and indifferent reception by consumers. Digital video cassettes include: Betacam IMX (Sony) D-VHS (JVC) D1 (Sony) D2 (Sony) D3 D5 HD Digital-S D9 (JVC) Digital Betacam (Sony) Digital8 (Sony) DV HDV ProHD (JVC) MiniDV MicroMV == Analog cassettes used as digital data storage == Historically, the compact audio cassette which was originally designed for analog storage of music was used as an alternative to disk drives in the late 1970s and early 1980s to provide data storage for home computers. There is a number of unique and incompatible cassette tape data storage formats that all use the same analog compact audio cassette tape media. The ADAT system uses Super VHS tapes to record 8 synchronized digital audiotracks at once. There have also been several audio recording systems that used VHS video recorders as storage devices and video tape transports, generally by encoding the digital data to be recorded into an analog composite video signal (which resembles static) and then recording this to magnetic tape. These systems were often used as "mixdown" recorders, to record the finished mix from a multi-track recorder in preparation for the manufacture of a vinyl record, cassette tape, or CD. An example was the Dbx Model 700. Another example is the Sony PCM adaptor series. Several companies sold VHS backup solutions in the 1980s and 1990s where data was converted to a video image which was then saved onto a VHS tape. the Corvus "Mirror" ( U.S. patent 4380047A ) the Metrum Model 64 on S-VHS tape, the Danmere Backer tape backup system, the Alpha Microsystems Videotrax the Legacy Storage Systems International VAST (Variable Array Storage) the ArVid the Video Backup System Amiga, The S2 VLBI system at three NASA Deep Space Network complexes and over 20 other radio telescopes stores digital data on SVHS tapes.
Electronic kit
An electronic kit is a package of electrical components used to build an electronic device. Generally, kits are composed of electronic components, a circuit diagram (schematic), assembly instructions, and often a printed circuit board (PCB) or another type of prototyping board. There are two types of kits. Some build a single device or system. Other types used for education demonstrate a range of circuits. These will include a solderless construction board of some type, such as: Components mounted in plastic blocks with side contacts, that are held together in a base, e.g. Denshi blocks Springs on a card board, the springs trap wire leads, or component leads, such as Philips EE electronic experiment kits. These are a cheap and flexible option Professional type prototyping boards, (breadboards) into which component leads are inserted, following documentation of the "kit". The first type of kit for constructing a single device normally uses a PCB on which components are soldered. They normally come with extended documentation describing which component goes where into the PCB. For advanced hobby projects, sometimes the kit may only consist of a printed circuit board and assembly instructions, and the purchaser may have to source all the parts independently; or, the vendor may provide hard-to-get or pre-programmed parts while expecting the purchaser to obtain the rest of the components. People primarily purchase electronic kits to have fun and learn how things work. They were once popular as a means to reduce the cost of buying goods, but there is usually no cost saving in buying a kit today. Some electronic kits were assembled to make complete complex devices such as color television sets, oscilloscopes, high-end audio amplifiers, amateur radio equipment, electric organs, and even computers such as the Heathkit H-8, and the LNW-80. Many of the early microprocessor computers were sold as either electronic kits or assembled and tested. Heathkit sold millions of electronic kits during its 45-year history. Home assembly of common consumer electronics items no longer provides a cost advantage over commercially manufactured and distributed devices. People still build kits for custom devices and special-purpose electronics for professional and educational use and as a hobby. Also emerging is a trend to simplify the complexity by providing preprogrammed or modular kits often provided by many suppliers online. The fun and thrill of making your own electronics have shifted, in many cases, from easy-to-comprehend applications and analog devices to more sophisticated digital devices. == Examples == The Altair 8800 (the first home computer) was also sold as a kit, as were the MK14, Sinclair ZX80, Sinclair ZX81 and Acorn Atom computers. Many S-100 bus system cards were sold only as kits. Building a Robot kit, most often with a micro controller inside, is now in fashion.
ZygoteBody
ZygoteBody, formerly Google Body, is a web application by Zygote Media Group that renders manipulable 3D anatomical models of the human body. Several layers, from muscle tissues down to blood vessels, can be removed or made transparent to allow better study of individual body parts. Most of the body parts are labelled and are searchable. == Technology == The human models are based on data from the Zygote Media Group. The website uses JavaScript and WebGL technology to display 3D images inside the web browser without requiring the installation of external browser plug-ins. == History == ZygoteBody was launched as Google Body on December 15, 2010. On April Fools' Day 2011, users were greeted with the anatomy of a cow on the home page. The cow model is still available as part of the open-3d-viewer open source project. As part of the wind down on Google Labs, it was announced that Google Body will be shut down but will continue to be maintained by Zygote as ZygoteBody. On October 13, 2011, the Google Body site was shut down. Then, on January 9, 2012, ZygoteBody was launched and core code base (with the Google Cow model as a demo) was made available as an open source project called open-3d-viewer.
Mini-STX
Mini-STX (mSTX, Mini Socket Technology EXtended, originally "Intel 5x5") is a computer motherboard form factor that was released by Intel in 2015 (as "Intel 5x5"). These motherboards measure 147mm by 140mm (5.8" x 5.5"), making them larger than "4x4" NUC (102x102mm / 4.01" x 4.01" inches) and Nano-ITX (120x120mm / 4.7" x 4.7") boards, but notably smaller than the more common Mini-ITX (170x170mm / 6.7" x 6.7") boards. Unlike these standards, which use a square shape, the Mini-STX form factor is 7mm longer from front-to-rear, making it slightly rectangular. == Mini-STX design elements == The Mini-STX design suggests (but does not require) support for: Socketed processors (e.g. LGA or PGA CPUs) Onboard power regulation circuitry, enabling direct DC power input IO ports embedded on the front and rear of the motherboard (akin to NUC, but unlike typical motherboards which often use headers instead to connect built-in ports on enclosures) == Adoption by manufacturers == This motherboard form factor is still not in particularly common use with consumer-PC manufacturers, although there are a few offerings: ASRock offers both DeskMini kits (that use mini-STX boards) and standalone motherboards, Asus offer VivoMini kits (that use mini-STX boards) and standalone motherboards, Gigabyte offers a few motherboards, and industrial PC suppliers (e.g. Kontron, Iesy, ASRock Industrial) also provide some options for mini-STX equipment. == Derivatives == ASRock developed a derivative of mini-STX, dubbed micro-STX, for their 'DeskMini GTX/RX' small form-factor PCs and industrial motherboards. Micro-STX adds an MXM slot which allows the use of special PCI Express expansion cards, including graphics or machine learning accelerators, but increases the width of the board to be extended two inches, resulting in measurements of 147 x 188 mm (5.8" x 7.4")