AI Data Room

AI Data Room — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Quickly (software)

Quickly is a framework for creating software programs for a Linux distribution using Python, PyGTK, Glade Interface Designer and Desktop Couch. It then allows for easy publishing using bzr and Launchpad. Quickly is designed to speed up the start of new projects with the use of templates, not only for programs but for any type of project. These templates are used to automate project configuration and maintenance. Delegating into templates and not into a specific library allows projects created using Quickly not to require dependencies on any particular library or runtime of Quickly itself. The project was started by Rick Spencer after his frustration as a beginner Ubuntu developer. == Updates == Last available software update is on 2013-01-31 for Ubuntu 11.04.
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Solid-state electronics

Solid-state electronics are semiconductor electronics: electronic equipment that use semiconductor devices such as transistors, diodes and integrated circuits (ICs). The term is also used as an adjective for devices in which semiconductor electronics that have no moving parts replace devices with moving parts, such as the solid-state relay, in which transistor switches are used in place of a moving-arm electromechanical relay, or the solid-state drive (SSD), a type of semiconductor memory used in computers to replace hard disk drives, which store data on rotating disks. == History == The term solid-state became popular at the beginning of the semiconductor era in the 1960s to distinguish this new technology. A semiconductor device works by controlling an electric current consisting of electrons or holes moving within a solid crystalline piece of semiconducting material such as silicon, while the thermionic vacuum tubes it replaced worked by controlling a current of electrons or ions in a vacuum within a sealed tube. Although the first solid-state electronic device was the cat's whisker detector, a crude semiconductor diode invented around 1904, solid-state electronics started with the invention of the transistor in 1947. Before that, all electronic equipment used vacuum tubes, because vacuum tubes were the only electronic components that could amplify—an essential capability in all electronics. The transistor, which was invented by John Bardeen and Walter Houser Brattain while working under William Shockley at Bell Laboratories in 1947, could also amplify, and replaced vacuum tubes. The first transistor hi-fi system was developed by engineers at GE and demonstrated at the University of Philadelphia in 1955. In terms of commercial production, The Fisher TR-1 was the first "all transistor" preamplifier, which became available mid-1956. In 1961, a company named Transis-tronics released a solid-state amplifier, the TEC S-15. The replacement of bulky, fragile, energy-hungry vacuum tubes by transistors in the 1960s and 1970s created a revolution not just in technology but in people's habits, making possible the first truly portable consumer electronics such as the transistor radio, cassette tape player, walkie-talkie and quartz watch, as well as the first practical computers and mobile phones. Other examples of solid state electronic devices are the microprocessor chip, LED lamp, solar cell, charge coupled device (CCD) image sensor used in cameras, and semiconductor laser. Also during the 1960s and 1970s, television set manufacturers switched from vacuum tubes to semiconductors, and advertised sets as "100% solid state" even though the cathode-ray tube (CRT) was still a vacuum tube. It meant only the chassis was 100% solid-state, not including the CRT. Early advertisements spelled out this distinction, but later advertisements assumed the audience had already been educated about it and shortened it to just "100% solid state". LED displays can be said to be truly 100% solid-state.
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Style sheet (web development)

A web style sheet is a form of separation of content and presentation for web design in which the markup (i.e., HTML or XHTML) of a webpage contains the page's semantic content and structure, but does not define its visual layout (style). Instead, the style is defined in an external style sheet file using a style sheet language such as CSS or XSLT. This design approach is identified as a "separation" because it largely supersedes the antecedent methodology in which a page's markup defined both style and structure. The philosophy underlying this methodology is a specific case of separation of concerns. == Benefits == Separation of style and content has advantages, but has only become practical after improvements in popular web browsers' CSS implementations. === Speed === Overall, users experience of a site utilising style sheets will generally be quicker than sites that do not use the technology. ‘Overall’ as the first page will probably load more slowly – because the style sheet AND the content will need to be transferred. Subsequent pages will load faster because no style information will need to be downloaded – the CSS file will already be in the browser’s cache. === Maintainability === Holding all the presentation styles in one file can reduce the maintenance time and reduces the chance of error, thereby improving presentation consistency. For example, the font color associated with a type of text element may be specified — and therefore easily modified — throughout an entire website simply by changing one short string of characters in a single file. The alternative approach, using styles embedded in each individual page, would require a cumbersome, time consuming, and error-prone edit of every file. === Accessibility === Sites that use CSS with either XHTML or HTML are easier to tweak so that they appear similar in different browsers (Chrome, Internet Explorer, Mozilla Firefox, Opera, Safari, etc.). Sites using CSS "degrade gracefully" in browsers unable to display graphical content, such as Lynx, or those so very old that they cannot use CSS. Browsers ignore CSS that they do not understand, such as CSS 3 statements. This enables a wide variety of user agents to be able to access the content of a site even if they cannot render the style sheet or are not designed with graphical capability in mind. For example, a browser using a refreshable braille display for output could disregard layout information entirely, and the user would still have access to all page content. === Customization === If a page's layout information is stored externally, a user can decide to disable the layout information entirely, leaving the site's bare content still in a readable form. Site authors may also offer multiple style sheets, which can be used to completely change the appearance of the site without altering any of its content. Most modern web browsers also allow the user to define their own style sheet, which can include rules that override the author's layout rules. This allows users, for example, to bold every hyperlink on every page they visit. Browser extensions like Stylish and Stylus have been created to facilitate management of such user style sheets. === Consistency === Because the semantic file contains only the meanings an author intends to convey, the styling of the various elements of the document's content is very consistent. For example, headings, emphasized text, lists and mathematical expressions all receive consistently applied style properties from the external style sheet. Authors need not concern themselves with the style properties at the time of composition. These presentational details can be deferred until the moment of presentation. === Portability === The deferment of presentational details until the time of presentation means that a document can be easily re-purposed for an entirely different presentation medium with merely the application of a new style sheet already prepared for the new medium and consistent with elemental or structural vocabulary of the semantic document. A carefully authored document for a web page can easily be printed to a hard-bound volume complete with headers and footers, page numbers and a generated table of contents simply by applying a new style sheet.
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FactorDaily

FactorDaily is an Indian digital media publication founded in 2016 by Pankaj Mishra and Jayadevan PK. Mishra was formerly an Editor at TechCrunch and the Economic Times. The digital publication was launched with an intent to produce stories on the impact of technology on life in India. == History == FactorDaily began publishing in May 2016, with daily reported stories on technology, culture and life in India. Prior to its launch, the company had raised $1 million in seed funding from Accel India, Blume Ventures, Girish Mathrubootham of Freshdesk, Vijay Shekhar Sharma of PayTm, and Jay Vijayan of Tekion. Josey Puliyenthuruthel John, formerly Managing Editor at Business Today and National Corporate Editor at Mint, later joined the company as a Consulting Editor. In January 2017, FactorDaily launched its first Podcast called The Outliers. The inaugural episode featured a conversation with Manish Sharma of Printo on his journey starting up. == Awards == The FactorDaily team won the Bengaluru Editors Lab 2017, a journalism hackathon organised by the Global Editors Network (GEN). The story titled "India has 3,800 psychiatrists for 1.2bn people. Can tech step in to manage mental health?" won the first prize in the online category of the fifth Schizophrenia Research Foundation’s (SCARF) ‘Media for Mental Health’ awards. The story titled 'The dark hand of tech that stokes sex trafficking in India', won the Stop Slavery media Awards by the Thomson Reuters Foundation for the year 2020.
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Buckeye Corpus

The Buckeye Corpus of conversational speech is a speech corpus created by a team of linguists and psychologists at Ohio State University led by Prof. Mark Pitt. It contains high-quality recordings from 40 speakers in Columbus, Ohio conversing freely with an interviewer. The interviewer's voice is heard only faintly in the background of these recordings. The sessions were conducted as Sociolinguistics interviews, and are essentially monologues. The speech has been orthographically transcribed and phonetically labeled. The audio and text files, together with time-aligned phonetic labels, are stored in a format for use with speech analysis software (Xwaves and Wavesurfer). Software for searching the transcription files is also available at the project web site. The corpus is available to researchers in academia and industry. The project was funded by the National Institute on Deafness and Other Communication Disorders and the Office of Research at Ohio State University.
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HTTP compression

HTTP compression is a capability that can be built into web servers and web clients to improve transfer speed and bandwidth utilization. HTTP data is compressed before it is sent from the server: compliant browsers will announce what methods are supported to the server before downloading the correct format; browsers that do not support compliant compression method will download uncompressed data. The most common compression schemes include gzip and Brotli; a full list of available schemes is maintained by the IANA. There are two different ways compression can be done in HTTP. At a lower level, a Transfer-Encoding header field may indicate the payload of an HTTP message is compressed. At a higher level, a Content-Encoding header field may indicate that a resource being transferred, cached, or otherwise referenced is compressed. Compression using Content-Encoding is more widely supported than Transfer-Encoding, and some browsers do not advertise support for Transfer-Encoding compression to avoid triggering bugs in servers. == Compression scheme negotiation == The negotiation is done in two steps, described in RFC 2616 and RFC 9110: 1. The web client advertises which compression schemes it supports by including a list of tokens in the HTTP request. For Content-Encoding, the list is in a field called Accept-Encoding; for Transfer-Encoding, the field is called TE. 2. If the server supports one or more compression schemes, the outgoing data may be compressed by one or more methods supported by both parties. If this is the case, the server will add a Content-Encoding or Transfer-Encoding field in the HTTP response with the used schemes, separated by commas. The web server is by no means obligated to use any compression method – this depends on the internal settings of the web server and also may depend on the internal architecture of the website in question. == Content-Encoding tokens == The official list of tokens available to servers and client is maintained by IANA, and it includes: br – Brotli, a compression algorithm specifically designed for HTTP content encoding, defined in RFC 7932 and implemented in all modern major browsers. compress – UNIX "compress" program method (historic; deprecated in most applications and replaced by gzip or deflate) deflate – compression based on the deflate algorithm (described in RFC 1951), a combination of the LZ77 algorithm and Huffman coding, wrapped inside the zlib data format (RFC 1950); exi – W3C Efficient XML Interchange gzip – GNU zip format (described in RFC 1952). Uses the deflate algorithm for compression, but the data format and the checksum algorithm differ from the "deflate" content-encoding. This method is the most broadly supported as of March 2011. identity – No transformation is used. This is the default value for content coding. pack200-gzip – Network Transfer Format for Java Archives zstd – Zstandard compression, defined in RFC 8478 In addition to these, a number of unofficial or non-standardized tokens are used in the wild by either servers or clients: bzip2 – compression based on the free bzip2 format, supported by lighttpd lzip – compression based on the free lzip format, supported by wget and Links lzma – compression based on (raw) LZMA is available in Opera 20, and in elinks via a compile-time option peerdist – Microsoft Peer Content Caching and Retrieval rsync – delta encoding in HTTP, implemented by a pair of rproxy proxies. xpress – Microsoft compression protocol used by Windows 8 and later for Windows Store application updates. LZ77-based compression optionally using a Huffman encoding. xz – LZMA2-based content compression, supported by a non-official Firefox patch; and fully implemented in mget since 2013-12-31. == Servers that support HTTP compression == SAP NetWeaver Microsoft IIS: built-in or using third-party module Apache HTTP Server, via mod_deflate (despite its name, only supporting gzip), and mod_brotli Hiawatha HTTP server: serves pre-compressed files Cherokee HTTP server, On the fly gzip and deflate compressions Oracle iPlanet Web Server Zeus Web Server lighttpd nginx – built-in Applications based on Tornado, if "compress_response" is set to True in the application settings (for versions prior to 4.0, set "gzip" to True) Jetty Server – built-into default static content serving and available via servlet filter configurations GeoServer Apache Tomcat IBM Websphere AOLserver Ruby Rack, via the Rack::Deflater middleware HAProxy Varnish – built-in. Works also with ESI Armeria – Serving pre-compressed files NaviServer – built-in, dynamic and static compression Caddy – built-in via encode Many content delivery networks also implement HTTP compression to improve speedy delivery of resources to end users. The compression in HTTP can also be achieved by using the functionality of server-side scripting languages like PHP, or programming languages like Java. Various online tools exist to verify a working implementation of HTTP compression. These online tools usually request multiple variants of a URL, each with different request headers (with varying Accept-Encoding content). HTTP compression is considered to be implemented correctly when the server returns a document in a compressed format. By comparing the sizes of the returned documents, the effective compression ratio can be calculated (even between different compression algorithms). == Problems preventing the use of HTTP compression == A 2009 article by Google engineers Arvind Jain and Jason Glasgow states that more than 99 person-years are wasted daily due to increase in page load time when users do not receive compressed content. This occurs when anti-virus software interferes with connections to force them to be uncompressed, where proxies are used (with overcautious web browsers), where servers are misconfigured, and where browser bugs stop compression being used. Internet Explorer 6, which drops to HTTP 1.0 (without features like compression or pipelining) when behind a proxy – a common configuration in corporate environments – was the mainstream browser most prone to failing back to uncompressed HTTP. Another problem found while deploying HTTP compression on large scale is due to the deflate encoding definition: while HTTP 1.1 defines the deflate encoding as data compressed with deflate (RFC 1951) inside a zlib formatted stream (RFC 1950), Microsoft server and client products historically implemented it as a "raw" deflated stream, making its deployment unreliable. For this reason, some software, including the Apache HTTP Server, only implements gzip encoding. == Security implications == Compression allows a form of chosen plaintext attack to be performed: if an attacker can inject any chosen content into the page, they can know whether the page contains their given content by observing the size increase of the encrypted stream. If the increase is smaller than expected for random injections, it means that the compressor has found a repeat in the text, i.e. the injected content overlaps the secret information. This is the idea behind CRIME. In 2012, a general attack against the use of data compression, called CRIME, was announced. While the CRIME attack could work effectively against a large number of protocols, including but not limited to TLS, and application-layer protocols such as SPDY or HTTP, only exploits against TLS and SPDY were demonstrated and largely mitigated in browsers and servers. The CRIME exploit against HTTP compression has not been mitigated at all, even though the authors of CRIME have warned that this vulnerability might be even more widespread than SPDY and TLS compression combined. In 2013, a new instance of the CRIME attack against HTTP compression, dubbed BREACH, was published. A BREACH attack can extract login tokens, email addresses or other sensitive information from TLS encrypted web traffic in as little as 30 seconds (depending on the number of bytes to be extracted), provided the attacker tricks the victim into visiting a malicious web link. All versions of TLS and SSL are at risk from BREACH regardless of the encryption algorithm or cipher used. Unlike previous instances of CRIME, which can be successfully defended against by turning off TLS compression or SPDY header compression, BREACH exploits HTTP compression which cannot realistically be turned off, as virtually all web servers rely upon it to improve data transmission speeds for users. As of 2016, the TIME attack and the HEIST attack are now public knowledge.
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News ticker

A news ticker (sometimes called a crawler, crawl, slide, zipper, ticker tape, or chyron) is a horizontal or vertical (depending on the language's writing system) text-based display either in the form of a graphic that typically resides in the lower third of the screen space on a television station or network (usually during news programming) or as a long, thin scoreboard-style display seen around the facades of some offices or public buildings dedicated to presenting headlines or minor pieces of news. It is an evolution of the paper strips tapes, a continuous paper print-out of stock quotes from a printing telegraph which was mainly used to transmit companies' share price information over telegraph lines before the advance of technology in the 1960s. News tickers have been used in Europe in countries such as United Kingdom, Germany and Ireland for some years; they are also used in several Asian countries and Australia. In the United States, tickers were long used on a special event basis by broadcast television stations to disseminate weather warnings, school closings, and election results. Sports telecasts occasionally used a ticker to update other contests in progress before the expansion of cable news networks and the internet for news content. In addition, some ticker displays are used to relay continuous business and financial information. Most tickers are traditionally displayed in the form of scrolling text running from right to left across the screen or building display (or in the opposite direction for right-to-left writing systems such as Arabic script and Hebrew), allowing for headlines of varying degrees of detail; some used by television broadcasters, however, display stories in a static manner (allowing for the seamless switching of each story individually programmed for display) or utilize a "flipping" effect (in which each individual headline is shown for a few seconds before transitioning to the next, instead of scrolling across the screen, usually resulting in a relatively quicker run through of all of the information programmed into the ticker). Since the growth in usage of the World Wide Web, some news tickers have syndicated news stories posted largely on websites of broadcasters or by other independent news agencies. == Current uses == === Television === The presentation of headlines or other information in a news ticker has become a common element of many different news networks. The use of the ticker has differed on a number of channels: News networks and local newscasts commonly use a setup in which news headlines are scrolled across an area near the bottom of the screen, though some variations have formed, such as showing one headline at a time with a scrolling or "flipper" effect. Financial news channels use two or more tickers displaying company shares prices and business headlines. Networks with a focus on sports often use a slightly different system, where scores and statuses of ongoing and finished games are displayed one by one, along with minor sports highlights, statistics and sports news headlines. They are typically divided into categories devoted to specific leagues and events (with college basketball and football usually focusing on the top 25 ranked teams on the AP Poll, occasionally supplemented by sections for specific conferences). Some programs, including news-based programs emphasizing viewer interactivity, or special events, may also use tickers to display messages and reactions from viewers and others that relate to the program. These comments are often sourced from social networking services such as Facebook and Twitter, typically curating comments from a specific page or hashtag. Due to their current prevalence, they have been occasionally been made targets of pranks and vandalism. In one such example, News 14 Carolina allowed viewers to submit relevant information such as school closings or traffic delays via telephone or the Internet that would be incorporated into the ticker; the system was exploited in February 2004 to display humorous and crude messages, including the infamous "All your base are belong to us". Occasionally messages intended for training accidentally end up being put on the live ticker as happened on BBC News in 2022 when "Weather rain everywhere" and "Manchester United are rubbish" appeared on the live news ticker. Some businesses and organizations have utilized tickers intended for relaying weather-related closings as a surreptitious source for free guerrilla marketing, proclaiming they were open rather than closed and giving their phone number if possible, allowing them to 'advertise' on a television station all day for free. Since then, many stations have required pre-registration of businesses or organizations with an authorized representative and a signed affidavit on company letterhead affirming their authenticity, along with filtering out unfamiliar businesses and organizations, before being able to display their closing announcements. Stations also confirm all closings involving school districts with authorized officials to prevent situations in which students either show up to canceled classes in dangerous conditions, or do not attend school due to an erroneous, prank-submitted, or false listing. === On personal computers === Various applications have been developed over time to install news tickers on personal computer desktops using RSS feeds from news organizations, which are displayed in a fashion similar to those used by television channels but enable the user to access to underlying news stories, a feature not offered by traditional television channels. The Bloomberg Terminal and other financial information-tracking programs and devices also utilize tickers. A ticker may also be used as an unobtrusive method by businesses in order to deliver important information to their staff. The ticker can be set to reappear, stay on screen, or be put into a retractable mode (where a small tab is left visible on-screen). In the United Kingdom, broadcasters have stopped using this technology as other forms of communications have become available and increased in popularity. BBC News and Sky News discontinued their respective desktop tickers in March 2011 and 2012 to focus on other products, such as smartphone applications, to deliver updated information on breaking news and sport stories. === News tickers on buildings === Since the advent of the telegraph, newspapers commonly used their buildings to share the latest headlines. At first simple chalkboard signs were used for bulletins, but limelight illumination, electric lights, magic lantern projections, and other novel techniques were later employed. The method of using electric lights to spell out moving letters was invented by Frank C. Reilly (August 20, 1888 – April 10, 1947) and patented in 1923. Reilly called his invention the Motograph News Bulletin. In 1928, The New York Times installed a Motograph News Bulletin to display news headlines on the sides of Times Tower. The display was 388 feet (118 m) long, 5 feet (1.5 m) high, and employed over 14,800 light bulbs. Popularly known as the "Zipper", the sign remained in use until the building was sold in 1961. The sign was darkened during World War II to comply with wartime lighting restrictions. The Motograph operated until 1994 and was replaced by an electronic version in 1995, which was in turn removed in 2017 due to the replacement of all individual screens on the front of One Times Square with a 350 foot (110 m)-tall LED billboard in 2018. Ticker displays appear today on the exterior of the News Corp Building, which houses the headquarters for Fox News Channel/News Corp in the west extension of Manhattan's Rockefeller Center, as well as one that displays delayed stock market data that is located in Times Square. NASDAQ itself features a large display screen on the facade of the NASDAQ MarketSite building in Times Square. The Reuters buildings at Canary Wharf and in Toronto have news and stock tickers; the latter type features market data for the New York Stock Exchange, NASDAQ and London Stock Exchange, while the Toronto building's ticker also includes quotes from the Toronto Stock Exchange. A red-LED ticker was added to the perimeter of 10 Rockefeller Center in 1994, as the building was being renovated to accommodate the studios for NBC's Today. Placed at the juncture of the first and second floors, the ticker is visible to spectators in Rockefeller Plaza and passersby on West 49th Street and updates continuously, even at times when Today is not being produced and broadcast. As of 2015, the ticker strip is only a small part of a large two-floor LCD video display that is placed within the window of the studio showing promotional information. The Martin Place Headquarters of Seven News, the news division of Australian television broadcaster Seven Network, also incorporates a ticker that wraps around the building. == In popular culture == The use of new
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Go-box

Go-box is a name used for a number of electronic devices. The "Go-Box" is often a box, crate, carry-case, modified briefcase or similar construction containing electronic equipment pre-setup and ready to function. The box can then be taken into the field or placed at a remote site with minimal effort. These are often used by radio amateurs (or "Hams") for emergency communications, experimental work, or field communications. This has also led to similar equipment being used in the Emergency Services, utility companies, military, and government agencies. A search of the YouTube website can reveal a number of ideas for these devices mostly built by people at home. Terms created after the use of "go-box" include the "go-bag" which is an 'essentials' bag of items needed for evacuations or quick departures, i.e. medicines, clothes, torch, Broadcast radio receiver, batteries, etc. In Austria it is a radio transmitter used in trucks as part of the Videomaut toll collection system. One use of the term in the United States it is a device which is supposed to change traffic signals from red to green. U.S. Fire trucks have a similar device, called an Opticon, that uses an infrared beam. Two residents of Miami, Florida, were arrested for selling fake go-boxes online. Several hundred were sold, prices ranging from $69 to $150. In reality, the boxes contained nothing more than strobe lights.
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Machine learning in video games

Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems. Information on machine learning techniques in the field of games is mostly known to public through research projects as most gaming companies choose not to publish specific information about their intellectual property. The most publicly known application of machine learning in games is likely the use of deep learning agents that compete with professional human players in complex strategy games. There has been a significant application of machine learning on games such as Atari/ALE, Doom, Minecraft, StarCraft, and car racing. Other games that did not originally exists as video games, such as chess and Go have also been affected by the machine learning. == Overview of relevant machine learning techniques == === Deep learning === Deep learning is a subset of machine learning which focuses heavily on the use of artificial neural networks (ANN) that learn to solve complex tasks. Deep learning uses multiple layers of ANN and other techniques to progressively extract information from an input. Due to this complex layered approach, deep learning models often require powerful machines to train and run on. ==== Convolutional neural networks ==== Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn translation invariant patterns, which are patterns that are not dependent on location. CNNs are able to learn these patterns in a hierarchy, meaning that earlier convolutional layers will learn smaller local patterns while later layers will learn larger patterns based on the previous patterns. A CNN's ability to learn visual data has made it a commonly used tool for deep learning in games. === Recurrent neural network === Recurrent neural networks are a type of ANN that are designed to process sequences of data in order, one part at a time rather than all at once. An RNN runs over each part of a sequence, using the current part of the sequence along with memory of previous parts of the current sequence to produce an output. These types of ANN are highly effective at tasks such as speech recognition and other problems that depend heavily on temporal order. There are several types of RNNs with different internal configurations; the basic implementation suffers from a lack of long term memory due to the vanishing gradient problem, thus it is rarely used over newer implementations. ==== Long short-term memory ==== A long short-term memory (LSTM) network is a specific implementation of a RNN that is designed to deal with the vanishing gradient problem seen in simple RNNs, which would lead to them gradually "forgetting" about previous parts of an inputted sequence when calculating the output of a current part. LSTMs solve this problem with the addition of an elaborate system that uses an additional input/output to keep track of long term data. LSTMs have achieved very strong results across various fields, and were used by several monumental deep learning agents in games. === Reinforcement learning === Reinforcement learning is the process of training an agent using rewards and/or punishments. The way an agent is rewarded or punished depends heavily on the problem; such as giving an agent a positive reward for winning a game or a negative one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks and others. It has seen strong performance in both the field of games and robotics. === Neuroevolution === Neuroevolution involves the use of both neural networks and evolutionary algorithms. Instead of using gradient descent like most neural networks, neuroevolution models make use of evolutionary algorithms to update neurons in the network. Researchers claim that this process is less likely to get stuck in a local minimum and is potentially faster than state of the art deep learning techniques. == Deep learning agents == Machine learning agents have been used to take the place of a human player rather than function as NPCs, which are deliberately added into video games as part of designed gameplay. Deep learning agents have achieved impressive results when used in competition with both humans and other artificial intelligence agents. === Chess === Chess is a turn-based strategy game that is considered a difficult AI problem due to the computational complexity of its board space. Similar strategy games are often solved with some form of a Minimax Tree Search. These types of AI agents have been known to beat professional human players, such as the historic 1997 Deep Blue versus Garry Kasparov match. Since then, machine learning agents have shown ever greater success than previous AI agents. === Go === Go is another turn-based strategy game which is considered an even more difficult AI problem than chess. The state space of is Go is around 10^170 possible board states compared to the 10^120 board states for Chess. Prior to recent deep learning models, AI Go agents were only able to play at the level of a human amateur. ==== AlphaGo ==== Google's 2015 AlphaGo was the first AI agent to beat a professional Go player. AlphaGo used a deep learning model to train the weights of a Monte Carlo tree search (MCTS). The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance of a given state. The deep learning model allows the agent to explore potential game states more efficiently than a vanilla MCTS. The network were initially trained on games of humans players and then were further trained by games against itself. ==== AlphaGo Zero ==== AlphaGo Zero, another implementation of AlphaGo, was able to train entirely by playing against itself. It was able to quickly train up to the capabilities of the previous agent. === StarCraft series === StarCraft and its sequel StarCraft II are real-time strategy (RTS) video games that have become popular environments for AI research. Blizzard and DeepMind have worked together to release a public StarCraft 2 environment for AI research to be done on. Various deep learning methods have been tested on both games, though most agents usually have trouble outperforming the default AI with cheats enabled or skilled players of the game. ==== Alphastar ==== Alphastar was the first AI agent to beat professional StarCraft 2 players without any in-game advantages. The deep learning network of the agent initially received input from a simplified zoomed out version of the gamestate, but was later updated to play using a camera like other human players. The developers have not publicly released the code or architecture of their model, but have listed several state of the art machine learning techniques such as relational deep reinforcement learning, long short-term memory, auto-regressive policy heads, pointer networks, and centralized value baseline. Alphastar was initially trained with supervised learning, it watched replays of many human games in order to learn basic strategies. It then trained against different versions of itself and was improved through reinforcement learning. The final version was hugely successful, but only trained to play on a specific map in a protoss mirror matchup. === Dota 2 === Dota 2 is a multiplayer online battle arena (MOBA) game. Like other complex games, traditional AI agents have not been able to compete on the same level as professional human player. The only widely published information on AI agents attempted on Dota 2 is OpenAI's deep learning Five agent. ==== OpenAI Five ==== OpenAI Five utilized separate long short-term memory networks to learn each hero. It trained using a reinforcement learning technique known as Proximal Policy Learning running on a system containing 256 GPUs and 128,000 CPU cores. Five trained for months, accumulating 180 years of game experience each day, before facing off with professional players. It was eventually able to beat the 2018 Dota 2 esports champion team in a 2019 series of games. === Planetary Annihilation === Planetary Annihilation is a real-time strategy game which focuses on massive scale war. The developers use ANNs in their default AI agent. === Supreme Commander 2 === Supreme Commander 2 is a real-time strategy (RTS) video game. The game uses Multilayer Perceptrons (MLPs) to control a platoon’s reaction to encountered enemy units. Total of four MLPs are used, one for each platoon type: land, naval
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Problematic social media use

Excessive use of social media can lead to problems including impaired functioning and a reduction in overall wellbeing, for both users and those around them. Such usage is associated with a risk of mental health problems, sleep problems, academic struggles, and daytime fatigue. Psychological or behavioural dependence on social media platforms can result in significant negative functions in peoples daily lives. The risk of problems is also related to the type of platform of social media or online community being used. People of different ages and genders may be affected in different ways by problematic social media use. == Signs and symptoms == Signs of social media addiction or excessive use of social media include many behaviours similar to substance use disorders, including mood modification, salience, tolerance, stress withdrawal symptoms, psychological distress, anxiety and depression, conflict, and relapse, and low self esteem. People with problematic social media habits are at risk of being addicted and may require more time on social media as time passes. Frequent social media use may also be associated with self-reported symptoms of attention deficit hyperactivity disorder. Social anxiety (or fear of missing out) is another potential symptom. Social anxiety is defined as having intense anxiety or fear of being judged, negatively evaluated, or rejected in a social or performance situation. The fear of missing out can contribute to excessive usage due to frequent checking the media constantly throughout the day to check in and see what others are doing instead of doing other activities. Common signs include displacement, or replacing meaningful other activities with social media, and loneliness. == Causes and mechanisms == There are many theories for the mechanism or cause behind a person having problematic social media use. The transition from normal to problematic social media use occurs when a person relies on it to relieve stress, loneliness, depression, or provide continuous rewards. Cognitive-behavioral model – People increase their use of social media when they are in unfamiliar environments or awkward situations; Social skill model – People pull out their phones and use social media when they prefer virtual communication as opposed to face-to-face interactions because they lack self-presentation skills; Socio-cognitive model – This person uses social media because they love the feeling of people liking and commenting on their photos and tagging them in pictures. They are attracted to the positive outcomes they receive on social media. There are parallels to the gambling industry inherent to the design of various social media sites, with "'ludic loops' or repeated cycles of uncertainty, anticipation and feedback" potentially contributing to problematic social media use. Another factor directly facilitating the development of addiction to social media is the implicit attitude toward the IT artifact. Social media use may also stimulate the reward pathway in the brain. There is also a theory that social media addiction fulfills a basic evolutionary drives in the wake of mass urbanization worldwide. The basic psychological needs of "secure, predictable community life that evolved over millions of years" remain unchanged, leading some to find online communities to cope with the new individualized way of life in some modern societies. The "Evolutionary Mismatch" hypothesis holds that modern digital platforms amplify social competition and comparison in ways our ancestors never faced, possibly triggering maladaptive patterns such as anxiety, depression, or compulsive use. Similarly, some scholars compare social media to "junk food": The approach taken to develop social media platforms may contribute to problematic social media use. The ability to scroll and stream content endlessly and how app developers distort time by affecting the 'flow' of content when scrolling, potentially resulting in the Zeigarnik effect (the human brain will continue to pursue an unfinished task until a satisfying closure. Autoplay modes, the personalized nature of the content results in emotional attachment (the user values this above its actual value, which is referred to as the endowment effect), and the exposure effect (repeated exposure to a distinct stimulus by the user can condition the user into an enhanced or improved attitude toward it). The interactive nature of the platforms, including the ability to "like" content has also been linked. Even though social media can satisfy personal communication needs, those who use it at higher rates are shown to have higher levels of psychological distress. == Diagnosis == While there is no official diagnostic term or measurement, problematic social media use is conceptualized as a non-substance-related disorder, resulting in preoccupation and compulsion to engage excessively in social media platforms despite negative consequences. No diagnosis exists for problematic social media use in either the ICD-11 or DSM-5. Excessive use of an activity, like social media, does not directly equate with addiction. There are other factors that could lead to someone's social media addiction including personality traits and pre-existing tendencies. While the extent of social media use and addiction are positively correlated, it is erroneous to employ use (the degree to which one makes use of the site's features, the effort exerted during use sessions, access frequency, etc.) as a proxy for addiction. Indicators of a potential dependence on social media include: Mood swings: a person uses social media to regulate his or her mood, or as a means of escaping real world conflicts. Relevance: social media starts to dominate a person's thoughts at the expense of other activities. Salience: social media becomes the most important part of someone's life. Tolerance: a person increases their time spent on social media to experience previously associated feelings they had while using social media. Withdrawal: when a person can not access social media their sleeping or eating habits change or signs of depression or anxiety can become present. Conflicts in real life: when social media is used excessively, it can affect real-life relationships with family and friends. Relapse: the tendency for previously affected individuals to revert to previous patterns of excessive social media use. There have been several scales developed and validated that help to understand the issues regarding problematic social media use. There is not one single scale that is being used by all researchers. == Treatment == Screen time recommendations for children and families have been developed by the American Academy of Pediatrics. Possible therapeutic interventions published include: Self-help interventions, including application-specific timers; Cognitive behavioural therapy; and Organisational and schooling support. Medications have not been shown to be effective in randomized, controlled trials for the related conditions of Internet addiction disorder or gaming disorder. == Prevention == Prevention approaches include screen time monitoring apps and other tech-based approaches to improve efficiency and decrease screen time and tools to help with addiction to online platform products. Parents' methods for monitoring, regulating, and understanding their children's social media use are referred to as parental mediation. Parental mediation strategies include active, restrictive, and co-using methods. Active mediation involves direct parent-child conversations that are intended to educate children on social media norms and safety, as well as the variety and purposes of online content. Restrictive mediation entails the implementation of rules, expectations, and limitations regarding children's social media use and interactions. Co-use is when parents jointly use social media alongside their children, and is most effective when parents are actively participating (like asking questions, making inquisitive/supportive comments) versus being passive about it. Active mediation is the most common strategy used by parents, though the key to success for any mediation strategy is consistency/reliability. When parents reinforce rules inconsistently, have no mediation strategy, or use highly restrictive strategies for monitoring their children's social media use, there is an observable increase in children's aggressive behaviours. When parents openly express that they are supportive of their child's autonomy and provide clear, consistent rules for media use, problematic usage and aggression decreases. Knowing that consistent, autonomy-supportive mediation has more positive outcomes than inconsistent, controlling mediation, parents can consciously foster more direct, involved, and genuine dialogue with their children. This can help prevent or reduce problematic social media use in children and teenagers. == Outcomes == === Adolescents and teens === Increased social medi
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General time- and transfer constant analysis

The general time- and transfer-constants (TTC) analysis is the generalized version of the Cochran-Grabel (CG) method, which itself is the generalized version of zero-value time-constants (ZVT), which in turn is the generalization of the open-circuit time constant method (OCT). While the other methods mentioned provide varying terms of only the denominator of an arbitrary transfer function, TTC can be used to determine every term both in the numerator and the denominator. Its denominator terms are the same as that of Cochran-Grabel method, when stated in terms of time constants (when expressed in Rosenstark notation). however, the numerator terms are determined using a combination of transfer constants and time constants, where the time constants are the same as those in CG method. Transfer constants are low-frequency ratios of the output variable to input variable under different open- and short-circuited active elements. In general, a transfer function (which can characterize gain, admittance, impedance, trans-impedance, etc., based on the choice of the input and output variables) can be written as: H ( s ) = a 0 + a 1 s + a 2 s 2 + … + a m s m 1 + b 1 s + b 2 s 2 + … + b n s n {\displaystyle H(s)={\frac {a_{0}+a_{1}s+a_{2}s^{2}+\ldots +a_{m}s^{m}}{1+b_{1}s+b_{2}s^{2}+\ldots +b_{n}s^{n}}}} == The denominator terms == The first denominator term b 1 {\textstyle b_{1}} can be expressed as the sum of zero value time constants (ZVTs): b 1 = ∑ i = 1 N τ i 0 {\displaystyle b_{1}=\sum _{i=1}^{N}\tau _{i}^{0}} where τ i 0 {\textstyle \tau _{i}^{0}} is the time constant associated with the reactive element i {\textstyle i} when all the other sources are zero-valued (hence the superscript '0'). Setting a capacitor value to zero corresponds to an open circuit, while a zero-valued inductor is a short circuit. So for calculation of the τ i 0 {\textstyle \tau _{i}^{0}} , all other capacitors are open-circuited and all other inductors are short-circuited. This is the essence of the ZVT method, which reduces to OCT when only capacitors are involved. All independent sources are also zero-valued during the time constant calculations (voltage sources short-circuited and current source open-circuited). In this case, if the element in question (element i {\textstyle i} ) is a capacitor, the time constant is given by τ i 0 = R i 0 C i {\displaystyle \tau _{i}^{0}=R_{i}^{0}C_{i}} and when element i {\textstyle i} is an inductor is it given by: τ i 0 = L i / R i 0 {\displaystyle \tau _{i}^{0}=L_{i}/R_{i}^{0}} . where in both cases, the resistance R i 0 {\textstyle R_{i}^{0}} , is the resistance seen by elements i {\textstyle i} (denoted by subscript), when all the other elements are zero-valued (denoted by the zero superscript). The second-order denominator term is equal to: b 2 = ∑ i = 1 N − 1 ∑ j = i + 1 N τ i 0 τ j i = ∑ i 1 ⩽ i ∑ j < j ⩽ N τ i 0 τ j i {\displaystyle b_{2}=\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}\tau _{i}^{0}\tau _{j}^{i}=\sum _{i}^{1\leqslant i}\sum _{j}^{ Read more →
Webedia

Webedia S.A. is a company specializing in online media, a subsidiary of the Fimalac group based in Levallois-Perret, France. Webedia is active in more than twenty countries including France (AlloCiné, Jeuxvideo.com, MGG, Puremédias, Ode, Pureshopping, Volum, Terrafemina, 750g, easyVoyage, l’Automobile Magazine, Le 10 Sport), Brazil (AdoroCinema, Tudo Gostoso, Minhavida), Germany (Filmstarts, Moviepilot, GameStar), Spain and Latin America (Xataka, SensaCine, Raiser Games), Poland (Gry-Online and GetHero) and the United States (Boxoffice Pro). == History == === Early years (2007-2013) === Webedia was created in France in 2007, following the successive launches of the websites Purepeople, Puretrend and Purefans. Webedia bought the comparison shopping website Shopoon in 2008 and renamed it Pureshopping, and the website Ozap (media news) from M6 group in 2011 and renamed it Puremédias. Webedia was acquired by Fimalac in May 2013 and became its Internet media subsidiary. === Growth (2013-2016) === In 2013, Fimalac acquired AlloCiné, the websites Newsring and Youmag, the cooking website 750g and the cultural platform Exponaute. In 2014, Webedia acquired OverBlog, Jeuxvideo.com (through L'Odyssée Interactive and moved to Paris in 2015), Moviepilot (Germany), and Gameo Consulting (owner of Millenium, electronic sports), In December 2014, Webedia announced a license agreement with Ziff Davis to launch sites under the IGN franchise in Brazil and France at the beginning of 2015. The French version of IGN was launched on 2, it targets the general public and casual gamers. In 2015, Webedia acquired Côté Ciné Group (technological solutions for movie theaters and specialized press magazines: BoxOffice Pro in the United States and Côté Ciné in France), 57% of Easyvoyage group (online travel comparators Easyvol and Alibabuy, Mixicom (website JeuxActu and multi-channel network), 50% of the Brazilian network Paramaker, and West World Media (digital marketing company for the film industry). In 2016, Webedia bought Scimob (mobile video game studio), Surprizemi (home-delivered surprise boxes), Eklablog (blogging platform) Oxent (eSports World Convention), and Bang Bang Management (sports PR agency). In addition, an agreement is made with Paris Saint-Germain for Webedia to recruit and manage e-sports players on behalf of Paris Saint-Germain eSports. On November 15, 2016, the LFP announced that it had reached an agreement with beIN Sports and Webedia for the broadcasting of the first edition of the e-League 1. The competition is renewed for two additional seasons on July 26, 2017, the broadcasting agreements are renewed. On December 8, 2016, Webedia joined forces with Chronopost to launch Pourdebon, a home delivery service that connects Internet users and labeled producers (AOC, organic AB, etc.). Webedia has a slight majority (53%) in this new platform. === 2017 === On January 19, 2017, Webedia announced the acquisition of the English company Peach Digital, specializing in web development and digital marketing for movie theaters. In February 2017, Le Figaro announced that Webedia had invested 10 million euros in Illico Fresco, a home delivery service for baskets of recipes. The same month, FDJ and Webedia announced a partnership for the creation of eSports competitions: a professional one (FDJ Masters League) and another one for amateur gamers (FDJ Open Series) starting in March 2017. They are broadcast on Webedia's Web TV. At the end of February 2017, the media group finalized the acquisition of MyPoseo, a SaaS publisher specialized on SEO analytics. On March 8, 2017, Webedia launched LeStream, a Twitch Web TV dedicated to video games, the result of two years of development, in the company of several YouTubers including Cyprien and Squeezie,. On March 29, 2017, Webedia bought the Brazilian web publisher Minha Vida, a website devoted to health, nutrition, beauty and fitness, which attracts 14.3 million unique monthly visitors. Webedia reaches 44 million unique visitors in Brazil, and thus becomes the leading publisher on entertainment themes. In June 2017, the company made its largest international acquisition, with the American agency 3BlackDot, a media and marketing agency focused on videogamers. The agency, based in Los Angeles, manages 36 YouTubers followed by millions of subscribers on their channels which total 700 million videos viewed per month. In July 2017, Webedia bought IDZ, an audiovisual production company, and thus strengthened its production activities and its leadership on the YouTube channel networks in France. That year, Webedia was the first French media group to use the measurement of their global audiences by Comscore. It represents deduplicated coverage on desktops, laptops, smartphones and tablets, and includes audiences for websites, mobile applications and videos. This new measure allows Webedia to establish a deduplicated global audience of 177 million unique visitors in April 2017. In October 2017, Webedia announced its intention to launch a TV channel dedicated to electronic sports, called ES1. The channel was officially launched on January 10, 2018, on Orange TV and on February 6, 2018, on Free and Bouygues Telecom. In November 2017, Webedia, with the support of CDC International Capital, entered into exclusive negotiations with the Saudi company Uturn Entertainment, specializing in online entertainment, particularly on YouTube, and the production of digital content for the region's youth, with a view to merging it with Diwanee, a Webedia subsidiary in the Middle East, for an amount close to $100 million. In December 2017, Webedia acquired a majority stake in the United States–based company called Creators Media, which brings together social and video production platforms specializing in popular culture and entertainment. That same month, Webedia joined forces with Elephant, Emmanuel Chain's audiovisual production company, to create a new content production label aimed at Millennials. === 2018-2019 === In January 2018, Webedia launched a sports marketing agency: Only Sports & Passions. That same month, Illico Fresco, specialist in the delivery of kit meals belonging to Webedia, joined forces with Weight Watchers, the world leader in slimming products. In April 2018, Webedia published new audience figures in partnership with Comscore, 188 million unique monthly visitors in December 2017, an increase of 6.2% compared to the previous measure dating from April 2017. The same month, Webedia unveils its ambitions concerning content production, as a partnership with the video game studio Focus Home Interactive is signed with a title "Fear the Wolves" already planned for 2018, co-production projects of films, cartoons or series are announced. In July 2018, Webedia bought the American authors company Full Fathom Five, a company that helps authors produce books, TV series, films and video games. In October 2018, Webedia announced that it was focusing on both esports clubs PSG Esports and LeStream Esport. The first one being geared towards international competitions and the second devoted mainly to the French esports scene. The "Millenium" brand is thus refocusing around its media activities and esports merchandising products, and the "Millenium esport club" being gradually closed. The same month, the company announced the acquisition of Weblogs, a Spanish-speaking website publisher, thereby strengthening its activity in Spain and Latin America. On October 22, 2018, Webedia announced the merger of BoxOffice magazine with Film Journal International. On November 13, 2018, Groupe SEB announced the acquisition from Webedia of 750g International, the international branch of the French recipe site 750g (the original French website 750g.com being retained by Webedia). The group is thus separating from Gourmandize (United States and United Kingdom), HeimGourmet (Germany), Rebañando (Spain), Receitas Sem Fronteiras (Brazil / Portugal) and Tribù Golosa (Italy). The same month, Webedia joined forces with Riot Games to launch the French League of League of Legends (LFL), the first French professional league on the League of Legends game, which will bring together the 8 best teams on the French scene. In March 2019, Webedia bought 51% of the audiovisual production company Elephant. The new set will weigh 500 million euros, a quarter of which will be made outside France. The same month, Webedia purchased a majority stake in the company Partoo, which publishes a SaaS platform specializing in local marketing for brands and merchants. On March 14, 2019, a new measurement of the international audience of Webedia sites was produced by Comscore, posting 250 million unique visitors in December 2018, up 9.2% compared to December 2017. In June 2019, the group joined forces with Michel Cymes, a famous doctor and French TV host by taking a majority stake in his company Club Santé Débat, in order to develop a health platform around the Dr. Good! Brand. In Sep
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Distributed concurrency control

Distributed concurrency control is the concurrency control of a system distributed over a computer network (Bernstein et al. 1987, Weikum and Vossen 2001). In database systems and transaction processing (transaction management) distributed concurrency control refers primarily to the concurrency control of a distributed database. It also refers to the concurrency control in a multidatabase (and other multi-transactional object) environment (e.g., federated database, grid computing, and cloud computing environments. A major goal for distributed concurrency control is distributed serializability (or global serializability for multidatabase systems). Distributed concurrency control poses special challenges beyond centralized one, primarily due to communication and computer latency. It often requires special techniques, like distributed lock manager over fast computer networks with low latency, like switched fabric (e.g., InfiniBand). The most common distributed concurrency control technique is strong strict two-phase locking (SS2PL, also named rigorousness), which is also a common centralized concurrency control technique. SS2PL provides both the serializability and strictness. Strictness, a special case of recoverability, is utilized for effective recovery from failure. For large-scale distribution and complex transactions, distributed locking's typical heavy performance penalty (due to delays, latency) can be saved by using the atomic commitment protocol, which is needed in a distributed database for (distributed) transactions' atomicity.
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1tik

1tik, pronounced Antik (Arabic: أنتيك; lit. "Everything is going well") is a fully Algerian instant messaging, social media and mobile payment app. designed, developed and built locally by the Algerian start-up, INTAJ Digital, with backing from the state-owned company ATM Mobilis (who's the company's main sponsor). It is described as Algeria's first super-app that is entirely designed and built by local developers. == Etymology == The name "1tik" (Arabic: أنتيك) is drawn from the popular Algerian vernacular (Antik), the neologism, which appeared several years ago, means "everything is going well" or "it's all good". == History == 1tik was officially launched and announced the 20th December 2025 by INTAJ Digital's founder Youcef Toulaib and a team of 50 employees, making it the first ever Algerian instant messaging, social media and mobile payment app, rivaling with the growing influence of Yassir in Algeria. it grew in popularity after the presidency of Algeria and several other state-owned companies, medias, and ministries opened official accounts on the app.
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Radio network

A radio network is a system that distributes radio signals to multiple receivers or enables two-way communication between stations and mobile units. Worldwide, radio networks include broadcast networks, such as BBC Radio in the United Kingdom and NPR in the United States, which transmit one-to-many signals for news, entertainment, and public information; two-way radio networks, used by police, fire services, taxicabs, and delivery fleets for operational communication; and cellular networks, such as Verizon, Vodafone, and China Mobile, which provide mobile telephony and data services using frequency or time division duplexing. While all rely on radio-frequency technology like transmitters, receivers, and antennas, their network architectures, protocols, and regulatory frameworks differ substantially across applications and regions. The two-way type of radio network shares many of the same technologies and components as the broadcast-type radio network but is generally set up with fixed broadcast points (transmitters) with co-located receivers and mobile receivers/transmitters or transceivers. In this way both the fixed and mobile radio units can communicate with each other over broad geographic regions ranging in size from small single cities to entire states/provinces or countries. There are many ways in which multiple fixed transmit/receive sites can be interconnected to achieve the range of coverage required by the jurisdiction or authority implementing the system: conventional wireless links in numerous frequency bands, fibre-optic links, or microwave links. In all of these cases the signals are typically backhauled to a central switch of some type where the radio message is processed and resent (repeated) to all transmitter sites where it is required to be heard. In contemporary two-way radio systems, a concept called trunking is commonly used to achieve better efficiency of radio spectrum use. It provides a very wide range of coverage, with no switching of channels required by the mobile radio user as it roams throughout the system coverage. Trunking of two-way radio is identical to the concept used for cellular phone systems where each fixed and mobile radio is specifically identified to the system controller and its operation is switched by the controller. == Broadcasting networks == The broadcast type of radio network is a network system which distributes radio programming to multiple stations simultaneously, or slightly delayed, for the purpose of extending total coverage beyond the limits of a single broadcast signal. The resulting expanded audience for radio programming or information essentially applies the benefits of mass-production to the broadcasting enterprise. A radio network has two sales departments, one to package and sell programs to radio stations, and one to sell the audience of those programs to advertisers. Most radio networks also produce much of their programming. Originally, radio networks owned some or all of the stations that broadcast the network's radio format programming. Presently however, there are many networks that do not own any stations and only produce and/or distribute programming. Similarly station ownership does not always indicate network affiliation. A company might own stations in several different markets and purchase programming from a variety of networks. Radio networks rose rapidly with the growth of regular broadcasting of radio to home listeners in the 1920s. This growth took various paths in different places. In Britain the BBC was developed with public funding, in the form of a broadcast receiver license, and a broadcasting monopoly in its early decades. In contrast, in the United States various competing commercial broadcasting networks arose funded by advertising revenue. In that instance, the same corporation that owned or operated the network often manufactured and marketed the listener's radio. Major technical challenges to be overcome when distributing programs over long distances are maintaining signal quality and managing the number of switching/relay points in the signal chain. Early on, programs were sent to remote stations (either owned or affiliated) by various methods, including leased telephone lines, pre-recorded gramophone records and audio tape. The world's first all-radio, non-wireline network was claimed to be the Rural Radio Network, a group of six upstate New York FM stations that began operation in June 1948. Terrestrial microwave relay, a technology later introduced to link stations, has been largely supplanted by coaxial cable, fiber, and satellite, which usually offer superior cost-benefit ratios. Many early radio networks evolved into television networks.
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