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Wix.com

Wix.com Ltd. (Hebrew: וויקס.קום, romanized: wix.com) or simply Wix is an Israeli software company, publicly listed in the US, that provides cloud-based web development services. It offers tools for creating HTML5 websites for desktop and mobile platforms using online drag-and-drop editing. Along with its headquarters and other offices in Israel, Wix also has offices in Brazil, Canada, Germany, India, Ireland, Japan, Lithuania, Poland, the Netherlands, the United States, Ukraine, and Singapore. Users can add applications for social media, e-commerce, online marketing, contact forms, e-mail marketing, and community forums to their websites. The Wix website builder is built on a freemium business model, earning its revenues through premium upgrades. According to the W3Techs technology survey website, Wix was used by 2.5% of websites as of September 2023; at the end of May 2025, it was 3.8%. == History == === Corporate affairs === Wix was founded in 2006 by Israeli developers Avishai Abrahami, Nadav Abrahami, and Giora Kaplan. With its main offices in Tel Aviv, Wix was backed by investors Insight Venture Partners, Mangrove Capital Partners, Bessemer Venture Partners, DAG Ventures, and Benchmark Capital. By April 2010, Wix had 3.5 million users and raised US$10 million in Series C funding provided by Benchmark Capital and existing investors Bessemer Venture Partners and Mangrove Capital Partners. In March 2011, Wix had 8.5 million users and raised US$40 million in Series D funding, bringing its total funding to that date to US$61 million. By August 2013, the Wix platform had more than 34 million registered users. On 5 November 2013, Wix had an initial public offering on NASDAQ, raising about US$127 million for the company and some share holders. In 2016, Mark Tluszcz became the chair of the board of directors. In 2020, Wix's revenue increased to $989 million, a 30% rise year-on-year, primarily due to the shift of businesses online during the coronavirus pandemic. The company added over 31 million new registered users in 2020, reaching a total of 196.7 million by year's end. Wix added approximately 1 million net new premium subscriptions in 2020, surpassing $1 billion in annual collections for the first time. By the end of the year, there were 5.5 million premium subscriptions, a 22% increase compared to the end of 2019. As of its most recent reporting in June 2024, Wix has over 260 million users worldwide. === Product development === ==== 2000s ==== Wix entered an open beta phase in 2007 using a platform based on Adobe Flash. ==== 2010s ==== In June 2011, Wix launched the Facebook store module, making its first step into social commerce. In March 2012, Wix launched a new HTML5 site builder, replacing the Adobe Flash technology. In October 2012, Wix launched an app market for users to sell applications built with the company's automated web development technology. In August 2014, Wix launched Wix Hotels, a booking system for hotels, bed and breakfasts, and vacation rentals that use Wix websites. In June 2016, Wix introduced Wix ADI (Artificial Design Intelligence), a platform that uses artificial intelligence to design websites. ==== 2020s ==== In 2020, Wix launched an additional CMS, EditorX, which included additional CSS features to the original builder. In July 2023, Wix announced that it would be building on its ADI technology to create an AI powered website generator In October 2023, Wix launched the Wix Studio website builder. Co-founder and CEO, Avishai Abrahami described the platform as a “product for agencies”. In March 2024, the AI web builder, which uses a chatbot to help users create content was launched to the public. In March 2025, the digital publisher CNET has identified Wix as the "Best overall website builder overall." In August 2025, Wix announced it would launch banking services—including checking accounts and loans for small businesses—via a partnership with Israeli fintech Unit Finance, as it sought to diversify amid what it described as threats to its core website-building business from artificial intelligence. In January 2026, Wix launched Wix Harmony. Wix harmony is an AI website builder that uses agentic technology, generative design and vibe coding—with manual editing features for additional control. In May 2026, Wix announced layoffs affecting approximately 1,000 employees, or 20% of its workforce. CEO Avishai Abrahami cited two factors: the need to restructure around artificial intelligence and the appreciation of the Israeli shekel against the US dollar, which increased the cost of its Israel-based workforce relative to its dollar-denominated revenue. === Acquisitions === In April 2014, Wix announced the acquisition of Appixia, an Israeli startup for creating native mobile commerce (mCommerce) apps. In October 2014, Wix announced its acquisition of OpenRest, a developer of online ordering systems for restaurants. In April 2015, Wix acquired Moment.me, a mobile website builder for events and marketing tools for social lead generation. On 23 February 2017, Wix acquired the online art community DeviantArt for US$36 million. In January 2017, the company acquired Flok, a provider of customer loyalty programs tools. In February 2020, Wix acquired Inkfrog for eBay sellers, a web design company that provides customized business management software for eBay sellers. On 2 March 2021, Wix acquired SpeedETab, a Miami-based restaurant online technology provider. In May 2021, Wix acquired Rise.ai, a gift card and customer re-engagement package for online brands. A month later, Wix acquired Modalyst, a marketplace and drop-shipping platform. In May 2025, Wix acquired Hour One, a startup specializing in AI-powered video creation tools, to enhance its generative AI capabilities. In June 2025, the company acquired Base44, owned by independent entrepreneur Maor Shlomo, with the intention of integrating Base44's artificial intelligence capabilities and conversational interface into Wix's website and app building platform. == Description == Wix uses a freemium business model. Users can create websites for free then must purchase premium packages to connect their sites to their own domains, remove Wix ads, access the form builder, add e-commerce capabilities, or buy extra data storage and bandwidth. Wix provides customizable website templates and a drag-and-drop HTML5 website builder that includes apps, graphics, image galleries, fonts, vectors, animations, and other options. Users also may opt to create their web sites from scratch. In October 2013, Wix introduced a mobile editor for mobile viewing customization. Wix App Market offers both free and subscription-based applications, with a revenue split of 80% for the developer and 20% for Wix. Customers can integrate third-party applications into their own web sites, such as photograph feeds, blogging, music playlists, online community, e-mail marketing, and file management. Custom JavaScript code can be inserted into Wix webpages using the Velo API. == Controversies == === Use of WordPress code === In October 2016, there was a controversy over Wix's use of WordPress's GPL-licensed code. In response, Avishai Abrahami, Wix's CEO, published a response describing which open-source code was used and how Wix says it collaborates with the open-source community. However, it was subsequently noted that collaboration with the open-source community was not sufficient under the terms of the GPL license, which requires any code built on GPL-licensed code to be released under the same license. === Censorship === On 31 May 2021, 2021 Hong Kong Charter, a Wix-hosted website run by exiled Hong Kong activists, was shut down at the request of the Hong Kong Police. This was the first known case of Hong Kong's National Security Law being used to censor content on an overseas website. Wix later apologized for "mistakenly removing the website" and reinstated the website after it had been down for four days. In October 2023, Wix fired an employee in Dublin, Ireland, for having made social media posts critical of Israel. This incident led to criticism of Wix from members of the Oireachtas (the Irish parliament) and from the head of the Irish government, Taoiseach Leo Varadkar, who said it was "not okay to dismiss somebody because of their political views". Deputy head of government, Tánaiste Micheál Martin, also condemned their dismissal, stating "we tolerate debate with freedom of speech, freedom of opinion, and people have different opinions on these issues." The dismissed employee, Courtney Carey, successfully sued the company for unfair dismissal. Wix did not contest the charge, admitting liability. === Outreach abroad === In October 2023, The Irish Times reported that an Israeli advertising agency advised Wix staff how they can tailor posts for "outreach abroad". This included advice for Wix employees to “show Westernity” in social media posts supporting Israel, stating that “unlike the Gazans,
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Scandiweb

scandiweb is a web development, digital strategy, AI consultation & implementation agency specializing in the Magento (Adobe Commerce) platform. The company was established in 2003 in Latvia by Antons Sapriko. It has offices in the United States, Sweden, Latvia, and Georgia. scandiweb provides solutions for primarily eCommerce businesses and acts as a strategic partner for IT development focusing on web, mobile, and big data analysis. T == Partnerships == scandiweb is an official Adobe Gold Partner, with the largest team of Adobe Commerce-certified employees. The company holds the Google Premier Partner status for 2025, placing it among top 3% agencies globally. scandiweb is a BigCommerce Certified Partner and a Pimcore Platinum Partner. Since 2016, scandiweb has been collaborating with Oro, Inc., an open-source business application development firm. scandiweb is a Platinum Partner of Hyvä, working with the Magento 2 frontend theme to optimize performance metrics. The company is also a Sanity Agency Partner, assisting with content management through Sanity’s headless CMS.
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Fansly

Fansly is a subscription-based social media platform that allows content creators to monetize exclusive content, including photos, videos, live streams, and direct messages. Operated by Select Media LLC, the platform is headquartered in Baltimore, Maryland. While the platform hosts a variety of content genres, it is primarily known for adult content and is frequently compared to OnlyFans. == History == Fansly was launched in 2020 by Micheal Etelis under Select Media LLC, which was incorporated in February 2020. The platform also operates through CY Media LTD, registered in Kamares, Cyprus, established in May 2021. The company has remained privately held with no disclosed external funding rounds or official valuation, operating as a bootstrapped entity. Based on Fansly's social media presence, which was created in November 2020, the platform did not begin gaining traction until early 2021 when creators started to become concerned about potential content policy changes at OnlyFans. In August 2021, OnlyFans announced it would ban sexually explicit content effective October 2021, citing pressure from banks involved in its payment processing. Although OnlyFans reversed the decision six days later, the announcement triggered a massive influx of users to Fansly; the platform received nearly 4,000 new creator applications in a single hour, causing its servers to crash from the surge in traffic. By August 21, 2021, Fansly had reached 2.1 million users. == Features and business model == Fansly operates as a B2C marketplace, taking a 20% commission on all transactions conducted on the platform, with creators retaining the remaining 80%. This commission rate is the same as that charged by its main competitor, OnlyFans. A distinguishing feature of Fansly is its tiered subscription model, which allows creators to set multiple subscription levels at different price points, each offering different perks such as exclusive content, chat access, or custom requests. By contrast, OnlyFans historically relied on a single-tier subscription model. Revenue streams on the platform include recurring subscriptions, one-time pay-per-view content purchases, tips, paid messaging, and live-streaming fees. The platform also features an algorithmic "For You" feed that helps users discover new creators, addressing a limitation of competitors that lack internal content promotion mechanisms. Additional features include content watermarking, geolocation blocking to control where content is visible, two-factor authentication, community polls, 24-hour stories, and social media integration with platforms such as Twitter and Twitch. Payouts are processed within one to two business days and support multiple methods, including bank transfers, Skrill, Paxum, and cryptocurrency. In December 2025, Fansly expanded its live-streaming capabilities, introducing ticketed access, private list gating, configurable chat permissions, stream goals, and interactive device integration. == Controversies == === OnlyFans anti-competitive allegations === In August 2022, a series of lawsuits were filed in the United States alleging that OnlyFans had bribed employees of Meta Platforms to place Instagram accounts of creators who also sold content on competitor platforms, including Fansly, onto a terrorist blacklist. The lawsuits alleged that adult performers had traffic driven away from their Instagram accounts after being falsely tagged as terror-related. OnlyFans denied awareness of such activity. The plaintiffs withdrew the bribery claim in July 2023, and the case was dismissed in August 2023. === Privacy class action === In June 2025, Select Media LLC (operating as Fansly) was the subject of a digital privacy class action lawsuit filed in Massachusetts District Court. The lawsuit alleged that the platform secretly collected and shared users' sensitive viewing data with Google and other third parties without consent. The case was brought on behalf of an estimated class of over 10,000 users across multiple states.
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Event cinema

Event cinema sometimes called alternative content cinema or livecasts refers to the use of movie theaters to display a varied range of live and recorded entertainment excluding traditional films, such as sport, opera, musicals, ballet, music, one-off TV specials, current affairs, comedy and religious services. == History and development == Event Cinema was set up at the start of the century with rock concerts by Bon Jovi (2001), David Bowie (2003), and Robbie Williams (2005) bringing non-film audiences into cinemas that had newly installed digital equipment. The Metropolitan Opera in New York through their partnership with Fathom Events is acknowledged as the trailblazer in this area, aggressively seeking out new markets and setting high standards for live broadcasts via satellite. Emulated by other opera houses worldwide such as the Royal Opera House following a close second, Glyndebourne, La Scala and the Sydney Opera House the genre of opera within the 'Event Cinema' industry has been a huge success, and has brought new, younger audiences into cash-strapped opera houses depended on state funding and wealthy benefactors for the first time - an unforeseen and happy consequence of digitisation. Ballet and theater have also been very successful, as have rock concerts, both live and recorded. The UK's National Theatre has been a huge success here with their season of live broadcasts under the banner 'NT Live', featuring big name casts such as Helen Mirren, whose recent turn as Queen Elizabeth II in The Audience was a sell out everywhere. (This was in partnership with another West End theatre and the NT are keen to help other theatres maximise their potential through live broadcasts). The Globe and the Royal Shakespeare Company are also producing work for live broadcast and recorded exhibition. As digitisation of cinemas matures, the Event Cinema industry is growing. The strongest territory is the US, followed by the UK and mainland European territories. Latin America is also a very strong market. Recent additions include Pompeii Live, a unique exhibition by the UK's British Museum, featuring celebrities and curators taking the audience on a live tour around the recreated set of Pompeii within the museum itself, and they are also exploring the schools market for the first time, following the live broadcast on June 18 with a daytime broadcast aimed at UK schools for the first time. If successful this will no doubt prove a model for future museums to emulate. An added incentive for exhibitors is the ability to show alternative content, i.e. alternative to mainstream, studio-driven content, such as live special events, sports, pre-show advertising and other digital or video content. In industry terms this has become known as 'Alternative Content', but has recently become known more widely as 'Event Cinema'. === Expanding markets === Some low-budget films that would normally not have a theatrical release because of distribution costs might be shown in smaller engagements than the typical large release studio pictures. The cost of duplicating a digital "print" is very low, so adding more theaters to a release has a small additional cost to the distributor. Movies that start with a small release could scale to a much larger release quickly if they were sufficiently successful, opening up the possibility that smaller movies could achieve box office success previously out of their reach. ==== Technical specifications ==== Event Cinema is also finding a market in 3rd world countries in which the higher costs and quality of DCI equipment are not yet affordable, as crucially there are no DCI specifications for Alternative Content as there is in mainstream [studio] content. This has led to an explosion in the variety of content on offer, but a lack of standardisation has led to questionable quality at times. As the industry matures, this lack of regulation is expected to change and there are moves afoot to introduce codes of practice and technical specifications. Recorded content complements mainstream studio content by maximising the 'downtime' that plagues the cinema industry, where screens worldwide spend a large proportion of their time in darkness and cinemas empty. Some cinema chains have targeted pensioners in particular, offering free tea and coffee for afternoon matinees of recorded opera, for example. Digital Cinema Packages (DCPs) have been useful to cinemas not yet equipped with satellite broadcasting capability and has enabled exhibitors to build their Event Cinema audience, which is not generally the 18-24 demographic that multiplexes are targeting. ==== New Audiences ==== Event Cinema has seen a return of an older, affluent audience, previously turned off by the multiplex experience, and cinemas are starting to capitalise on this by offering waiter-serviced, high class finger food and alcoholic beverages, complete with bars and restaurants, a world away from the traditional popcorn/soft drink model; art house cinemas are increasingly marketing themselves as 'destination' venues for an evening's entertainment, somewhere to spend an entire evening, rather than just a couple of hours. As exhibition admissions have plateau'd in recent years due to the explosion in VOD, tablet and mobile content technology, this new revenue stream has been a surprise and welcome addition to the cinema industry, though the US studios have been cautious in embracing the change as yet. The thrill of Live broadcasts means they are generally regarded as more popular than recorded events, but there are exceptions; artists with a loyal cult or teenage following tend to do particularly well in this area, as concert films featuring artists such as the Grateful Dead, Pearl Jam, JLS, Led Zeppelin and the Rolling Stones have shown. ==== The Future ==== As more and more distributors are emerging, offering an increasingly broad range of content to cinemas worldwide, the landscape itself is shifting: screen advertising companies, technical providers, and exhibitors themselves are reinventing themselves as Alternative Content or Event Cinema distributors, and the industry is witnessing a re-evaluation of business models and practices worldwide. Predictions are that this industry could be work in excess of US$1bn by 2015. An illustration of the growth of this industry is the news the establishment of a European trade association promoting the industry to the general public and supporting those involved in it and the Event Cinema Association.
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Ugly duckling theorem

The ugly duckling theorem is an argument showing that classification is not really possible without some sort of bias. More particularly, it assumes finitely many properties combinable by logical connectives, and finitely many objects; it asserts that any two different objects share the same number of (extensional) properties. The theorem is named after Hans Christian Andersen's 1843 story "The Ugly Duckling", because it shows that a duckling is just as similar to a swan as two swans are to each other. It was derived by Satosi Watanabe in 1969. == Mathematical formula == Suppose there are n things in the universe, and one wants to put them into classes or categories. One has no preconceived ideas or biases about what sorts of categories are "natural" or "normal" and what are not. So one has to consider all the possible classes that could be, all the possible ways of making a set out of the n objects. There are 2 n {\displaystyle 2^{n}} such ways, the size of the power set of n objects. One can use that to measure the similarity between two objects, and one would see how many sets they have in common. However, one cannot. Any two objects have exactly the same number of classes in common if we can form any possible class, namely 2 n − 1 {\displaystyle 2^{n-1}} (half the total number of classes there are). To see this is so, one may imagine each class is represented by an n-bit string (or binary encoded integer), with a zero for each element not in the class and a one for each element in the class. As one finds, there are 2 n {\displaystyle 2^{n}} such strings. As all possible choices of zeros and ones are there, any two bit-positions will agree exactly half the time. One may pick two elements and reorder the bits so they are the first two, and imagine the numbers sorted lexicographically. The first 2 n / 2 {\displaystyle 2^{n}/2} numbers will have bit #1 set to zero, and the second 2 n / 2 {\displaystyle 2^{n}/2} will have it set to one. Within each of those blocks, the top 2 n / 4 {\displaystyle 2^{n}/4} will have bit #2 set to zero and the other 2 n / 4 {\displaystyle 2^{n}/4} will have it as one, so they agree on two blocks of 2 n / 4 {\displaystyle 2^{n}/4} or on half of all the cases, no matter which two elements one picks. So if we have no preconceived bias about which categories are better, everything is then equally similar (or equally dissimilar). The number of predicates simultaneously satisfied by two non-identical elements is constant over all such pairs. Thus, some kind of inductive bias is needed to make judgements to prefer certain categories over others. === Boolean functions === Let x 1 , x 2 , … , x n {\displaystyle x_{1},x_{2},\dots ,x_{n}} be a set of vectors of k {\displaystyle k} booleans each. The ugly duckling is the vector which is least like the others. Given the booleans, this can be computed using Hamming distance. However, the choice of boolean features to consider could have been somewhat arbitrary. Perhaps there were features derivable from the original features that were important for identifying the ugly duckling. The set of booleans in the vector can be extended with new features computed as boolean functions of the k {\displaystyle k} original features. The only canonical way to do this is to extend it with all possible Boolean functions. The resulting completed vectors have 2 k {\displaystyle 2^{k}} features. The ugly duckling theorem states that there is no ugly duckling because any two completed vectors will either be equal or differ in exactly half of the features. Proof. Let x and y be two vectors. If they are the same, then their completed vectors must also be the same because any Boolean function of x will agree with the same Boolean function of y. If x and y are different, then there exists a coordinate i {\displaystyle i} where the i {\displaystyle i} -th coordinate of x {\displaystyle x} differs from the i {\displaystyle i} -th coordinate of y {\displaystyle y} . Now the completed features contain every Boolean function on k {\displaystyle k} Boolean variables, with each one exactly once. Viewing these Boolean functions as polynomials in k {\displaystyle k} variables over GF(2), segregate the functions into pairs ( f , g ) {\displaystyle (f,g)} where f {\displaystyle f} contains the i {\displaystyle i} -th coordinate as a linear term and g {\displaystyle g} is f {\displaystyle f} without that linear term. Now, for every such pair ( f , g ) {\displaystyle (f,g)} , x {\displaystyle x} and y {\displaystyle y} will agree on exactly one of the two functions. If they agree on one, they must disagree on the other and vice versa. (This proof is believed to be due to Watanabe.) == Discussion == A possible way around the ugly duckling theorem would be to introduce a constraint on how similarity is measured by limiting the properties involved in classification, for instance, between A and B. However Medin et al. (1993) point out that this does not actually resolve the arbitrariness or bias problem since in what respects A is similar to B: "varies with the stimulus context and task, so that there is no unique answer, to the question of how similar is one object to another". For example, "a barberpole and a zebra would be more similar than a horse and a zebra if the feature striped had sufficient weight. Of course, if these feature weights were fixed, then these similarity relations would be constrained". Yet the property "striped" as a weight 'fix' or constraint is arbitrary itself, meaning: "unless one can specify such criteria, then the claim that categorization is based on attribute matching is almost entirely vacuous". Stamos (2003) remarked that some judgments of overall similarity are non-arbitrary in the sense they are useful: "Presumably, people's perceptual and conceptual processes have evolved that information that matters to human needs and goals can be roughly approximated by a similarity heuristic... If you are in the jungle and you see a tiger but you decide not to stereotype (perhaps because you believe that similarity is a false friend), then you will probably be eaten. In other words, in the biological world stereotyping based on veridical judgments of overall similarity statistically results in greater survival and reproductive success." Unless some properties are considered more salient, or 'weighted' more important than others, everything will appear equally similar, hence Watanabe (1986) wrote: "any objects, in so far as they are distinguishable, are equally similar". In a weaker setting that assumes infinitely many properties, Murphy and Medin (1985) give an example of two putative classified things, plums and lawnmowers: "Suppose that one is to list the attributes that plums and lawnmowers have in common in order to judge their similarity. It is easy to see that the list could be infinite: Both weigh less than 10,000 kg (and less than 10,001 kg), both did not exist 10,000,000 years ago (and 10,000,001 years ago), both cannot hear well, both can be dropped, both take up space, and so on. Likewise, the list of differences could be infinite… any two entities can be arbitrarily similar or dissimilar by changing the criterion of what counts as a relevant attribute." According to Woodward, the ugly duckling theorem is related to Schaffer's Conservation Law for Generalization Performance, which states that all algorithms for learning of boolean functions from input/output examples have the same overall generalization performance as random guessing. The latter result is generalized by Woodward to functions on countably infinite domains.
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Scalable Video Coding

Scalable Video Coding (SVC) is a video compression standard developed jointly by the ITU-T and the ISO/IEC. The two organizations formed the Joint Video Team (JVT) to create the H.264/MPEG-4 AVC standard (ITU-T Rec. H.264 | ISO/IEC 14496-10 AVC). SVC aims to provide adaptable or scalable content, allowing a single encoded video stream to be decoded at various bitrates, resolutions, and quality levels, thus catering to diverse devices and network conditions. == History == In October 2003, the Moving Picture Experts Group (MPEG) issued a Call for Proposals on SVC Technology. Fourteen proposals were submitted, twelve of which utilized wavelet compression, while the remaining two were extensions of H.264/MPEG-4 AVC. The proposal from the Heinrich-Hertz-Institut (HHI) was selected by MPEG as the foundation for the SVC standardization project. In January 2005, MPEG and the Video Coding Experts Group (VCEG) agreed to finalize SVC as an amendment to the H.264/MPEG-4 AVC standard. In November 2008, Google launched Gmail Video Chat, which employed an H.264/SVC codec, marking the first consumer application of the standard. This service was succeeded by Google+ Hangouts in 2012. In 2011, Google Code highlighted SVC as the successor to the open-source RVC video chat engine, noting its prominence in 2010. == Principles of scalability == === Overview === Scalability refers to the ability to represent a video signal at multiple levels of detail within a single encoded bitstream. This enables decoding of a base layer for basic quality and additional enhancement layers for progressively higher quality. SVC defines three types of scalability: Spatial scalability: Supports multiple resolution levels. Temporal scalability: Enables varying frame rates. Quality scalability: Provides different image quality levels. === Spatial scalability === Spatial scalability allows the reconstruction of video at different resolutions, such as QCIF, CIF, or SD. This is achieved through a pyramidal decomposition into multiple spatial layers. === Temporal scalability === Temporal scalability adjusts the frame rate of the decoded video stream. Various frame rates are supported using a hierarchical structure of video frames. === Quality scalability === Quality scalability, or Signal-to-Noise Ratio (SNR) scalability, improves the signal-to-noise ratio of a layer, reducing quantization distortion between the original and reconstructed images. SVC supports two approaches: Fine Grain Scalability (FGS) and Coarse Grain Scalability (CGS). ==== Coarse Grain Scalability (CGS) ==== CGS incorporates quality scalability across spatial resolutions. Each spatial resolution is encoded as a separate layer, refining texture and motion data. For a given resolution, quality scalability is achieved by encoding multiple quality layers with progressively finer quantization steps, starting from a base layer with minimal quality. ==== Fine Grain Scalability (FGS) ==== FGS enables progressive refinement of transformed coefficients within a single spatial layer. The base quality layer is encoded using the AVC standard with an initial quantization parameter (QP) ensuring minimal acceptable quality. Subsequent refinement layers reduce the QP by six, halving the quantization step. The refinement data stream can be truncated at any point, allowing fine-grained quality scalability.
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Acquisition of DirecTV by AT&T

AT&T Inc. announced an agreement with the DirecTV Group on May 18, 2014, to acquire the company for $48.5 billion in a joint cash-stock transaction and assumed debts of $18.6 billion for a total offer of $67.1 billion. Due to stalling growth in the wireless sector, AT&T began diversifying into mass media to expand its consumer offerings. After regulatory agencies approved the purchase on July 24, 2015, AT&T briefly became the largest Pay-TV provider. DirecTV was brought under AT&T's communication segment and DirecTV Now was launched on November 30, 2016, as an alternative to cord-cutting. In the years following the purchase, DirecTV lost millions of subscribers across its satellite and streaming services and by 2019, calls grew for AT&T to divest itself off the business. Initially, AT&T rejected these calls and defended the acquisition, but by February 2021, it reached a deal with TPG Inc. to transfer ownership of DirecTV. Under the terms of the agreement, AT&T would retain a 70% majority stake in DirecTV but would no longer oversee its daily operations. The deal was finalized by August 2, 2021, with AT&T receiving $7.1 billion. By July 3, 2025, AT&T sold its majority stake to TPG, ending any ties of involvement. == Background and Development == === AT&T's history === The company to bear the name "AT&T" was founded on March 3, 1885, as American Telephone and Telegraph Company (or AT&T Corporation) by Theodore Newton Vail as a long-distance subsidiary of the Bell Telephone Company. By December 1899, the Bell Telephone's assets were transferred to AT&T, with the latter gaining control of the Bell System, a regional network of local telecom companies. Theodore Vail became AT&T's President in 1907 and under his leadership, AT&T gained a monopoly over the telephone sector in the United States. This near century dominance earned AT&T the nickname of "Ma Bell." In 1974, the U.S. Department of Justice sued AT&T on accounts of antitrust violations. AT&T challenged the lawsuit, but in 1982, it reached a settlement with the DOJ to break apart its Bell System monopoly into seven regional companies. On January 1, 1984, the Bell System came to an end and led to a reshaped telecom industry. One of these regional companies, Southwestern Bell, emerged as the smallest, but after the passage of the 1996 Telecom Act, deregulated telecom rules allowed SBC to become a major telecom company. AT&T briefly became the largest cable and broadband company by the end of the 20th Century, but later deconsolidated to exit those industries. In 2005, SBC acquired its former parent, AT&T, and took on its branding as AT&T Inc, while retaining its previous business history. The newly reincorporated AT&T acquired BellSouth in 2006 and reconstituted much of its former Bell System. === DirecTV's history === == Acquisition Timeline == == Managing DirecTV == == Divestment and Spinoff ==
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Comparison of JavaScript-based web frameworks

This is a comparison of web frameworks for front-end web development that are reliant on JavaScript code for their behavior. == General information == == High-level framework comparison == JavaScript-based web application frameworks, such as React and Vue, provide extensive capabilities but come with associated trade-offs. These frameworks often extend or enhance features available through native web technologies, such as routing, component-based development, and state management. While native web standards, including Web Components, modern JavaScript APIs like Fetch and ES Modules, and browser capabilities like Shadow DOM, have advanced significantly, frameworks remain widely used for their ability to enhance developer productivity, offer structured patterns for large-scale applications, simplify handling edge cases, and provide tools for performance optimization. Frameworks can introduce abstraction layers that may contribute to performance overhead, larger bundle sizes, and increased complexity. Modern frameworks, such as React 18 and Vue 3, address these challenges with features like concurrent rendering, tree-shaking, and selective hydration. While these advancements improve rendering efficiency and resource management, their benefits depend on the specific application and implementation context. Lightweight frameworks, such as Svelte and Preact, take different architectural approaches, with Svelte eliminating the virtual DOM entirely in favor of compiling components to efficient JavaScript code, and Preact offering a minimal, compatible alternative to React. Framework choice depends on an application’s requirements, including the team’s expertise, performance goals, and development priorities. A newer category of web frameworks, including enhance.dev, Astro, and Fresh, leverages native web standards while minimizing abstractions and development tooling. These solutions emphasize progressive enhancement, server-side rendering, and optimizing performance. Astro renders static HTML by default while hydrating only interactive parts. Fresh focuses on server-side rendering with zero runtime overhead. Enhance.dev prioritizes progressive enhancement patterns using Web Components. While these tools reduce reliance on client-side JavaScript by shifting logic to build-time or server-side execution, they still use JavaScript where necessary for interactivity. This approach makes them particularly suitable for performance-critical and content-focused applications. == Features == == Browser support ==
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Texture artist

A texture artist is an individual who develops textures for digital media, usually for video games, movies, web sites and television shows or things like 3D posters. These textures can be in the form of 2D or (rarely) 3D art that may be overlaid onto a polygon mesh to create a realistic 3D model. Texture artists often take advantage of web sites for the purposes of marketing their art and self-promotion of their skills with the goal of gaining employment from a professional game studio or to join a team working on a "mod" (modification) of an existing game in hopes of establishing industry or trade credentials.
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Attention inequality

Attention inequality is the inequality of distribution of attention across users on social networks, people in general, and for scientific papers. Yun Family Foundation introduced "Attention Inequality Coefficient" as a measure of inequality in attention and arguments it by the close interconnection with wealth inequality. == Relationship to economic inequality == Attention inequality is related to economic inequality since attention is an economically scarce good. The same measures and concepts as in classical economy can be applied for attention economy. The relationship develops also beyond the conceptual level—considering the AIDA process, attention is the prerequisite for real monetary income on the Internet. On data of 2018, a significant relationship between likes and comments on Facebook to donations is proven for non-profit organizations. == Attention economy == The attention economy refers to the practice of maximizing the attention users give to a product for advertising-related reasons. Attention economy remains one of the most common forms of advertising, and has been steadily increasing thanks to new technologies such as television, internet and social media. It is one of the most widely-used approaches to economy for its effectiveness for maximising the noticeability of a certain product. == Attention inequality in social media == In social media, attention inequality refers to the unequal distribution of users' attention on social media platforms. This means that instead of an equal distribution of attention, fewer sources receive a disproportionate share of attention, leaving many unnoticed. This phenomenon is possibly the result of social media algorithms, which are commonly designed to drive maximum engagement. This phenomenon is a large factor in the polarization and creation of echo-chambers. Social media algorithms tend to note content that is already performing well and display it to more users, while content that is equally engaging or well-made is not recommended to users. Posts that trigger strong emotions usually out-perform more "uncontroversial" content. When many users interact with the post, it signals the algorithm that the specific post drives engagement. The algorithm then tends to recommend that type of content to an exponential number of people, potentially outperforming "un-emotional" content. These factors, when combined, tend to create an unequal social media environment. == Attention inequality in science == According to a recent 2025 study about research inequality among scientists published in Information Processing and Management, scientific discourse is restricted to a small group of connected scientists, and is frequently not an accurate representation of the whole scientific community. Using citation-network analysis in the fields of nanoscience and chemical physics, the study claims that a group of connected scientists has a significant notability in the scientific community. The calculated connection strength between these scientists is estimated to be about 4.5, the study also says that these authors cite each other four times more often than would be predicted in a random network, whereas ordinary scientists that exist outside of this group only reach an estimated connection strength of 0.9. The study findings suggest that that scientific attention is not distributed by merit, but rather by the connectedness of the scientists involved in the research. == Extent == As data of 2008 shows, 50% of the attention is concentrated on approximately 0.2% of all hostnames, and 80% on 5% of hostnames. The Gini coefficient of attention distribution lay in 2008 at over 0.921 for such commercial domains names as ac.jp and at 0.985 for .org-domains. The Gini coefficient was measured on Twitter in 2016 for the number of followers as 0.9412, for the number of mentions as 0.9133, and for the number of retweets as 0.9034. For comparison, the world's income Gini coefficient was 0.68 in 2005 and 0.904 in 2018. More than 96% of all followers, 93% of the retweets, and 93% of all mentions are owned by 20% of Twitter. == Causes == At least for scientific papers, today's consensus states that inequality is unexplainable by variations of quality and individual talent. The Matthew effect plays a significant role in the emergence of attention inequality—those who already enjoy large amounts of attention get even more attention, and those who do not lose even more. Ranking algorithms based on relevance to the user have been found to alleviate the inequality of the number of posts across topics.
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Hardware-based encryption

Hardware-based encryption is the use of computer hardware to assist software, or sometimes replace software, in the process of data encryption. Typically, this is implemented as part of the processor's instruction set. For example, the AES encryption algorithm (a modern cipher) can be implemented using the AES instruction set on the ubiquitous x86 architecture. Such instructions also exist on the ARM architecture. However, more unusual systems exist where the cryptography module is separate from the central processor, instead being implemented as a coprocessor, in particular a secure cryptoprocessor or cryptographic accelerator, of which an example is the IBM 4758, or its successor, the IBM 4764. Hardware implementations can be faster and less prone to exploitation than traditional software implementations, and furthermore can be protected against tampering. == History == Prior to the use of computer hardware, cryptography could be performed through various mechanical or electro-mechanical means. An early example is the Scytale used by the Spartans. The Enigma machine was an electro-mechanical system cipher machine notably used by the Germans in World War II. After World War II, purely electronic systems were developed. In 1987 the ABYSS (A Basic Yorktown Security System) project was initiated. The aim of this project was to protect against software piracy. However, the application of computers to cryptography in general dates back to the 1940s and Bletchley Park, where the Colossus computer was used to break the encryption used by German High Command during World War II. The use of computers to encrypt, however, came later. In particular, until the development of the integrated circuit, of which the first was produced in 1960, computers were impractical for encryption, since, in comparison to the portable form factor of the Enigma machine, computers of the era took the space of an entire building. It was only with the development of the microcomputer that computer encryption became feasible, outside of niche applications. The development of the World Wide Web lead to the need for consumers to have access to encryption, as online shopping became prevalent. The key concerns for consumers were security and speed. This led to the eventual inclusion of the key algorithms into processors as a way of both increasing speed and security. == Implementations == === In the instruction set === ==== x86 ==== The X86 architecture, as a CISC (Complex Instruction Set Computer) Architecture, typically implements complex algorithms in hardware. Cryptographic algorithms are no exception. The x86 architecture implements significant components of the AES (Advanced Encryption Standard) algorithm, which can be used by the NSA for Top Secret information. The architecture also includes support for the SHA Hashing Algorithms through the Intel SHA extensions. Whereas AES is a cipher, which is useful for encrypting documents, hashing is used for verification, such as of passwords (see PBKDF2). ==== ARM ==== ARM processors can optionally support Security Extensions. Although ARM is a RISC (Reduced Instruction Set Computer) architecture, there are several optional extensions specified by ARM Holdings. === As a coprocessor === IBM 4758 – The predecessor to the IBM 4764. This includes its own specialised processor, memory and a Random Number Generator. IBM 4764 and IBM 4765, identical except for the connection used. The former uses PCI-X, while the latter uses PCI-e. Both are peripheral devices that plug into the motherboard. === Proliferation === Advanced Micro Devices (AMD) processors are also x86 devices, and have supported the AES instructions since the 2011 Bulldozer processor iteration. Due to the existence of encryption instructions on modern processors provided by both Intel and AMD, the instructions are present on most modern computers. They also exist on many tablets and smartphones due to their implementation in ARM processors. == Advantages == Implementing cryptography in hardware means that part of the processor is dedicated to the task. This can lead to a large increase in speed. In particular, modern processor architectures that support pipelining can often perform other instructions concurrently with the execution of the encryption instruction. Furthermore, hardware can have methods of protecting data from software. Consequently, even if the operating system is compromised, the data may still be secure (see Software Guard Extensions). == Disadvantages == If, however, the hardware implementation is compromised, major issues arise. Malicious software can retrieve the data from the (supposedly) secure hardware – a large class of method used is the timing attack. This is far more problematic to solve than a software bug, even within the operating system. Microsoft regularly deals with security issues through Windows Update. Similarly, regular security updates are released for Mac OS X and Linux, as well as mobile operating systems like iOS, Android, and Windows Phone. However, hardware is a different issue. Sometimes, the issue will be fixable through updates to the processor's microcode (a low level type of software). However, other issues may only be resolvable through replacing the hardware, or a workaround in the operating system which mitigates the performance benefit of the hardware implementation, such as in the Spectre exploit.
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M-DISC

M-DISC (Millennial Disc) is a write-once optical disc technology introduced in 2009 by Millenniata, Inc. and available as DVD and Blu-ray discs. == Overview == M-DISC's design is intended to provide archival media longevity. M-Disc claims that properly stored M-DISC DVD recordings will last up to 1000 years. The M-DISC DVD looks like a standard disc, except it is almost transparent with later DVD and BD-R M-Disks having standard and inkjet printable labels. The patents protecting the M-DISC technology assert that the data layer is a glassy carbon material that is substantially inert to oxidation and has a melting point of 200–1000 °C (392–1832 °F). M-Discs are readable by most regular DVD players made after 2005 and Blu-Ray and BDXL disc drives and writable by most made after 2011. Available recording capacities conform to standard DVD/Blu-ray sizes: 4.7 GB DVD+R to 25 GB BD-R, 50 GB BD-R and 100 GB BDXL. == History == M-DISC developer Millenniata, Inc. was co-founded by Brigham Young University professors Barry Lunt, Matthew Linford, CEO Henry O'Connell and CTO Doug Hansen. The company was incorporated on May 13, 2010, in American Fork, Utah. Millenniata, Inc. officially went bankrupt in December 2016. Under the direction of CEO Paul Brockbank, Millenniata had issued convertible debt. When the obligation for conversion was not satisfied, the company defaulted on the debt payment and the debt holders took possession of all of the company's assets. The debt holders subsequently started a new company, Yours.co, to sell M-DISCs and related services. As of the 2020s, there are only 2 licensed manufacturers of M-Discs: Ritek, sold under the Ritek and RiDATA brands, and Verbatim with co-branded discs, marketed as the "Verbatim M-DISC". 128 GB BDXL never made it to market due to the 2016 bankruptcy. Early in 2022, Verbatim changed the formulation of their M-DISC branded Blu-rays. These new discs could be written at a faster rate than the previous ones – 6× speed instead of 4×. The new discs also had different colouration and markings compared with older version. Later in the year customers accused Verbatim of selling an inferior product and deceptive marketing. Verbatim responded that the new discs were a further development of the older discs and should have the same longevity, and that the technical changes therein were responsible for the altered appearance and higher write speeds. The updated M-DISC currently sold on the market uses the same metal ablative layer (MABL) metal oxide inorganic recording layer used in many of Verbatim's regular Blu-ray products. == Durability claims == The original M-DISC DVD+R was tested according to ISO/IEC 10995:2011 and ECMA-379 with a projected rated lifespan of several hundred years in archival use. The glassy carbon layers, in theory if preserved correctly in an environment like a salt mine, could store the data for over 10,000 years before going outside of readable specifications. However, the polycarbonate plastics, which are commonly used by almost all optical media and heavily in CBRN and ballistic protective equipment due to their optical, physical impact and chemical resistant properties, have a lifespan rating of only around 1000 years before degradation. Verbatim Japan claims that M-DISCs now use a titanium layer to prevent moisture ingression and to provide environmental stability. M-DISCs sold in Japan are advertised to have a projected lifespan of 100 years or more based on internal ISO/IEC 16963 testing, while other regional Verbatim websites claim that M-DISCs have a projected lifespan of "several hundred years" based on ISO/IEC 16963 testing. == Durability testing == In 2009, testing was done by the US Department of Defense (DoD) producing the China Lake Report testing Millenniata's M-Disk DVD to current market offerings from Delkin, MAM-A, Mitsubishi, Taiyo Yuden and Verbatim with all brands using organic dyes failing to pass the series of accelerated aging tests. From 2010 to 2012, the French National Laboratory of Metrology and Testing (LNE) used high-temperature accelerated aging testing, at 90 °C (194 °F) and 85% relative humidity inside a CLIMATS Excal 5423-U, for 250 to 1000 hours with a mix of inorganic DVD+R discs from MPO, Verbatim, Maxell, Syylex and DataTresor. The summary of the tests states that Syylex Glass Master Disc was rated for 1000+ hours, DataTresor Disc 250 hours+ and M-Disk under 250 hours. The Syylex disc was a custom-ordered product that could not be burned in a consumer player when they were still purchaseable from Syylex before their bankruptcy, so it was not truly in the same category as the others. In 2016, a consumer Mol Smith did real world stress testing on the 25 GB BD-R M-Disc alongside TDK's standard BD-R 25 GB disc using a copied movie, which demonstrated the reliability of M-Disc's molding compared to standard discs; after 60 days of outdoor direct exposure the M-Disk was played without error, while the TDK disc was physically destroyed. In 2022, the NIST Interagency Report NIST IR 8387 listed the M-Disc as an acceptable archival format rated for 100+ years, citing the aforementioned 2009 and 2012 tests by the US Department of Defense and French National Laboratory of Metrology and Testing as sources. == Commercial support == While recorded discs are readable in conventional DVD and BD drives, M-disc DVDs can only be burned by drives with firmware that supports the slightly higher power mode that M-Disk requires for burning its inorganic layers, as such writing speed is typically 2× speed. Blu-ray M-discs can be both written and read in most standard Blu-ray drives and are certified by the Blu-ray Disc Association to meet all current standard specifications as of 2019. Typically, the M-Discs cost 1.5–3× the price of standard Blu-Ray discs with DVD M-Discs now having sparse availability. With the first-generation DVD M-DISCs, it was difficult to determine which was the writable side of the disc due to being near fully translucent, until coloring and later labels similar to that on standard DVD discs was added to discs to help distinguish the sides preventing user error. Asus, LG Electronics, Lite-On, Pioneer, Buffalo Technology, and Hitachi-LG produce drives that can record M-DISC media while Verbatim and Ritek produce M-DISC discs. == Adoption == The regional government of the U.S. state of Utah has used M-Disc since 2011. Some consumers and avid datahoarders have adopted the format for cold digital data storage. == Alternative technologies == === Optical === Syylex Glass Master Disc: these discs use etched glass and are only typically degradable by physical or chemical damage, but not by normal ageing inside an archival environment. Current BD 25 GB, BD-R DL 50 GB & BDXL 100 GB (three layer) and Sony's BDXL 128 GB (four layer) discs are rated for up to 50 years (Standard inorganic HTL discs). Sony's Optical Disc Archive, is an optical competitor to the LTO tape-based data storage system, currently with up to 5.5 TB cartridges of dual-sided 120mm discs, with desktop readers and automated rackmount standard archival systems allowing for large scale archival and data retrieval rated for an estimated 100+ years. Pioneer DM for Archive is a disc media and drive combination developed by Pioneer to meet the requirements laid out by the Japanese government for preservation of financial data for a minimum of 100 years. The discs use a MABL type recording layer and are manufactured with tight tolerances. Although burnable in any BD Writer, when burned in Pioneers DM for Archive writers using the DM Archiver software the media and burn quality meet ISO/IEC 18630 which defines the testing methods needed for ensuring media and burn quality. === Magnetic === Linear Tape-Open (LTO) is rated for up to 30 years in a climate-controlled environment and is currently in use by most industries, including broadcast and corporate digital data systems. The latest generation released in 2026 is LTO-10, it defines two unique cartridge types which can hold 30 TB or 40 TB each Hard disk drives are currently available up to 30 TB (HDD) capacity in 3.5-inch format and 5 TB in 2.5-inch laptop format. However, unlike optical media, they are limited to 5–25 years of operation lifespan due to inevitable mechanical failure or magnetic instability. == Gallery ==
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Self-supervised learning

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on externally-provided labels. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving them requires capturing essential features or relationships in the data. The input data is typically augmented or transformed in a way that creates pairs of related samples, where one sample serves as the input, and the other is used to formulate the supervisory signal. This augmentation can involve introducing noise, cropping, rotation, or other transformations. Self-supervised learning more closely imitates the way humans learn to classify objects. During SSL, the model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels, which help to initialize the model parameters. Next, the actual task is performed with supervised or unsupervised learning. Self-supervised learning has produced promising results in recent years, and has found practical application in fields such as audio processing, and is being used by Facebook and others for speech recognition. == Pseudo-labels == Pseudo-labels are automatically generated labels that a model assigns to unlabeled data based on its own predictions. They are widely used in self-supervised and semi-supervised learning, where ground-truth annotations are limited or unavailable. By treating predicted labels as surrogate ground truth, learning algorithms can make use of large quantities of unlabeled data in the training process. Pseudo-labeling also plays an important role in systems that must adapt to concept drift, where the statistical properties of the data change over time. In these scenarios, the model may detect that an incoming instance deviates from previously learned behavior. The system then generates a classification result for that instance, and this predicted class is used as a pseudo-label for updating or retraining model components that are becoming outdated. This approach enables continuous adaptation in dynamic environments without requiring manual annotation. In many adaptive learning pipelines, pseudo-labels are chosen when the classifier produces sufficiently confident predictions, reducing the risk of propagating errors. These pseudo-labeled instances are then incorporated into training to refresh or evolve the model's understanding of emerging data patterns, particularly when existing components show signs of “aging” due to drift or distributional shifts. This strategy reduces reliance on manual labeling while helping maintain long-term model performance. == Types == === Autoassociative self-supervised learning === Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original input. The term "autoassociative" comes from the fact that the model is essentially associating the input data with itself. This is often achieved using autoencoders, which are a type of neural network architecture used for representation learning. Autoencoders consist of an encoder network that maps the input data to a lower-dimensional representation (latent space), and a decoder network that reconstructs the input from this representation. The training process involves presenting the model with input data and requiring it to reconstruct the same data as closely as possible. The loss function used during training typically penalizes the difference between the original input and the reconstructed output (e.g. mean squared error). By minimizing this reconstruction error, the autoencoder learns a meaningful representation of the data in its latent space. === Contrastive self-supervised learning === For a binary classification task, training data can be divided into positive examples and negative examples. Positive examples are those that match the target. For example, if training a classifier to identify birds, the positive training data would include images that contain birds. Negative examples would be images that do not. Contrastive self-supervised learning uses both positive and negative examples. The loss function in contrastive learning is used to minimize the distance between positive sample pairs, while maximizing the distance between negative sample pairs. An early example uses a pair of 1-dimensional convolutional neural networks to process a pair of images and maximize their agreement. Contrastive Language-Image Pre-training (CLIP) allows joint pretraining of a text encoder and an image encoder, such that a matching image-text pair have image encoding vector and text encoding vector that span a small angle (having a large cosine similarity). InfoNCE (Noise-Contrastive Estimation) is a method to optimize two models jointly, based on Noise Contrastive Estimation (NCE). Given a set X = { x 1 , … x N } {\displaystyle X=\left\{x_{1},\ldots x_{N}\right\}} of N {\displaystyle N} random samples containing one positive sample from p ( x t + k ∣ c t ) {\displaystyle p\left(x_{t+k}\mid c_{t}\right)} and N − 1 {\displaystyle N-1} negative samples from the 'proposal' distribution p ( x t + k ) {\displaystyle p\left(x_{t+k}\right)} , it minimizes the following loss function: L N = − E X [ log ⁡ f k ( x t + k , c t ) ∑ x j ∈ X f k ( x j , c t ) ] {\displaystyle {\mathcal {L}}_{\mathrm {N} }=-\mathbb {E} _{X}\left[\log {\frac {f_{k}\left(x_{t+k},c_{t}\right)}{\sum _{x_{j}\in X}f_{k}\left(x_{j},c_{t}\right)}}\right]} === Non-contrastive self-supervised learning === Non-contrastive self-supervised learning (NCSSL) uses only positive examples. Counterintuitively, NCSSL converges on a useful local minimum rather than reaching a trivial solution, with zero loss. For the example of binary classification, it would trivially learn to classify each example as positive. Effective NCSSL requires an extra predictor on the online side that does not back-propagate on the target side. === Joint-Embedding and Predictive Architectures === A major class of self-supervised learning moves beyond contrastive pairs, instead maximizing the agreement between views while preventing collapse through statistical constraints. Rooted in Deep Canonical Correlation Analysis (Deep CCA), this approach includes Joint-Embedding Architectures (JEA) like Barlow Twins and VICReg, which enforce covariance constraints to learn invariant representations without negative sampling. Deep Latent Variable Path Modelling (DLVPM) generalizes this to multimodal systems, using path models to enforce correlation and orthogonality across diverse data types. In 2022 Yann LeCun introduced Joint-Embedding Predictive Architectures (JEPA) as a step towards decision making, reasoning, and autonomous human intelligence in machines, including self-improvement through autonomous learning. Founded in representation learning, LeCun included the concept of a “world model” in JEPA which aims to enable machines to replicate human intellect by providing machines with a concept for the world in which they exist. Unlike autoencoders, JEPAs operate entirely in latent space, avoiding pixel-level noise to focus on semantic structure. Rather than just learning invariance, JEPAs learn by predicting masked latent representations from visible context. JEPA has been applied to domains such as image analysis, audio processing, and motion in images and video. == Comparison with other forms of machine learning == SSL belongs to supervised learning methods insofar as the goal is to generate a classified output from the input. At the same time, however, it does not require the explicit use of labeled input-output pairs. Instead, correlations, metadata embedded in the data, or domain knowledge present in the input are implicitly and autonomously extracted from the data. These supervisory signals, extracted from the data, can then be used for training. SSL is similar to unsupervised learning in that it does not require labels in the sample data. Unlike unsupervised learning, however, learning is not done using inherent data structures. Semi-supervised learning combines supervised and unsupervised learning, requiring only a small portion of the learning data be labeled. In transfer learning, a model designed for one task is reused on a different task. Training an autoencoder intrinsically constitutes a self-supervised process, because the output pattern needs to become an optimal reconstruction of the input pattern itself. However, in current jargon, the term 'self-supervised' often refers to tasks based on a pretext-task training setup
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European Information Technology Observatory

The European Information Technology Observatory (EITO) gathers information on European and global markets for information technology, telecommunications and consumer electronics. The EITO is managed by Bitkom Research GmbH, a wholly owned subsidiary of BITKOM, the German Association for Information Technology, Telecommunications and New Media. EITO is sponsored by Deutsche Telekom, KPMG and Telecom Italia. The research activities of the EITO Task Force are supported by the European Commission and the OECD. The EITO exists thanks to an initiative of Enore Deotto from MIlan and the support of Luis-Alberto Petit Herrera (Madrid), Jörg Schomburg (Hanover) and Günther Möller (Frankfurt). Between 1993 and 2007, the market reports were published as printed annual reports ("EITO yearbook"). Since 2008 the market reports are available in electronic version and can be purchased on the EITO online portal. Currently, the ICT market reports are divided in following categories: International Reports International Reports include ICT market information of all EITO countries and all market segments or only specific segments. The newest ICT Market Report 2013/14, published in October 2013, includes market data of 36 countries: 28 European markets, BRIC countries, Japan, Turkey and the US as well as a deep analysis of ICT market developments in 9 European countries. The detailed market data and forecasts are available for the period 2010–2014. Country Reports This category includes EITO reports on a single country's ICT market. The Country ICT Market Reports are published biannually for France, Germany, Italy, Spain and the United Kingdom. Thematic Reports Thematic studies focusing on a specific topic. Customized Reports Market Reports made upon order.
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Web developer

A web developer is a programmer who develops World Wide Web applications using a client–server model. The applications typically use HTML, CSS, and JavaScript in the client, and any general-purpose programming language in the server. HTTP is used for communications between client and server. A web developer may specialize in client-side applications (Front-end web development), server-side applications (back-end development), or both (full-stack development). == Prerequisite == There are no formal educational or license requirements to become a web developer. However, many colleges and trade schools offer coursework in web development. There are also many tutorials and articles which teach web development, often freely available on the web - for example, on JavaScript. Even though there are no formal requirements, web development projects require web developers to have knowledge and skills such as: Using HTML, CSS, and JavaScript Programming/coding/scripting in one of the many server-side languages or frameworks Understanding server-side/client-side architecture and communication of the kind mentioned above Ability to utilize a database
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