Fresh Paint is a painting app developed by Microsoft and released on May 25, 2012. == History == Fresh Paint originated from a Microsoft Research project known as Project Gustav, an endeavor to reproduce the behavior of physical oil paint on a digital medium. To push the boundaries of simulating oil on a digital medium, the research team created a physics model that precisely replicated on a screen what would happen in the real world if you combined oil, a surface and a tool such as a paint brush. Two publications, Detail-Preserving Paint Modeling for 3D Brushes and Simple Data-Driven Modeling of Brushes, were released as a result of the team’s findings. After a variety of internal testing Project, Gustav was codenamed Digital Art. Partnering with The Museum of Modern Art, Digital Art was tested for a year by 60,000 people. With feedback culled from MoMA, developers expanded the existing physics model, experimenting with how real oil paint blended and reacted to the texture of a canvas. After final adjustments were made, Digital Art was rebranded as Fresh Paint. It was released to the public on 25 May 2012.
StyleGAN
The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN depends on Nvidia's CUDA software, GPUs, and Google's TensorFlow, or Meta AI's PyTorch, which supersedes TensorFlow as the official implementation library in later StyleGAN versions. The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. Nvidia introduced StyleGAN3, described as an "alias-free" version, on June 23, 2021, and made source available on October 12, 2021. == History == A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces. StyleGAN was able to run on Nvidia's commodity GPU processors. In February 2019, Uber engineer Phillip Wang used the software to create the website This Person Does Not Exist, which displayed a new face on each web page reload. Wang himself has expressed amazement, given that humans are evolved to specifically understand human faces, that nevertheless StyleGAN can competitively "pick apart all the relevant features (of human faces) and recompose them in a way that's coherent." In September 2019, a website called Generated Photos published 100,000 images as a collection of stock photos. The collection was made using a private dataset shot in a controlled environment with similar light and angles. Similarly, two faculty at the University of Washington's Information School used StyleGAN to create Which Face is Real?, which challenged visitors to differentiate between a fake and a real face side by side. The faculty stated the intention was to "educate the public" about the existence of this technology so they could be wary of it, "just like eventually most people were made aware that you can Photoshop an image". The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. In 2021, a third version was released, improving consistency between fine and coarse details in the generator. Dubbed "alias-free", this version was implemented with PyTorch. === Illicit use === In December 2019, Facebook took down a network of accounts with false identities, and mentioned that some of them had used profile pictures created with machine learning techniques. == Architecture == === Progressive GAN === Progressive GAN is a method for training GAN for large-scale image generation stably, by growing a GAN generator from small to large scale in a pyramidal fashion. Like SinGAN, it decomposes the generator as G = G 1 ∘ G 2 ∘ ⋯ ∘ G N {\displaystyle G=G_{1}\circ G_{2}\circ \cdots \circ G_{N}} , and the discriminator as D = D N ∘ D N − 1 ∘ ⋯ ∘ D 1 {\displaystyle D=D_{N}\circ D_{N-1}\circ \cdots \circ D_{1}} . During training, at first only G N , D N {\displaystyle G_{N},D_{N}} are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images. To avoid discontinuity between stages of the GAN game, each new layer is "blended in" (Figure 2 of the paper). For example, this is how the second stage GAN game starts: Just before, the GAN game consists of the pair G N , D N {\displaystyle G_{N},D_{N}} generating and discriminating 4x4 images. Just after, the GAN game consists of the pair ( ( 1 − α ) + α ⋅ G N − 1 ) ∘ u ∘ G N , D N ∘ d ∘ ( ( 1 − α ) + α ⋅ D N − 1 ) {\displaystyle ((1-\alpha )+\alpha \cdot G_{N-1})\circ u\circ G_{N},D_{N}\circ d\circ ((1-\alpha )+\alpha \cdot D_{N-1})} generating and discriminating 8x8 images. Here, the functions u , d {\displaystyle u,d} are image up- and down-sampling functions, and α {\displaystyle \alpha } is a blend-in factor (much like an alpha in image composing) that smoothly glides from 0 to 1. === StyleGAN === StyleGAN is designed as a combination of Progressive GAN with neural style transfer. The key architectural choice of StyleGAN-1 is a progressive growth mechanism, similar to Progressive GAN. Each generated image starts as a constant 4 × 4 × 512 {\displaystyle 4\times 4\times 512} array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance). At training time, usually only one style latent vector is used per image generated, but sometimes two ("mixing regularization") in order to encourage each style block to independently perform its stylization without expecting help from other style blocks (since they might receive an entirely different style latent vector). After training, multiple style latent vectors can be fed into each style block. Those fed to the lower layers control the large-scale styles, and those fed to the higher layers control the fine-detail styles. Style-mixing between two images x , x ′ {\displaystyle x,x'} can be performed as well. First, run a gradient descent to find z , z ′ {\displaystyle z,z'} such that G ( z ) ≈ x , G ( z ′ ) ≈ x ′ {\displaystyle G(z)\approx x,G(z')\approx x'} . This is called "projecting an image back to style latent space". Then, z {\displaystyle z} can be fed to the lower style blocks, and z ′ {\displaystyle z'} to the higher style blocks, to generate a composite image that has the large-scale style of x {\displaystyle x} , and the fine-detail style of x ′ {\displaystyle x'} . Multiple images can also be composed this way. === StyleGAN2 === StyleGAN2 improves upon StyleGAN in two ways. One, it applies the style latent vector to transform the convolution layer's weights instead, thus solving the "blob" problem. The "blob" problem roughly speaking is because using the style latent vector to normalize the generated image destroys useful information. Consequently, the generator learned to create a "distraction" by a large blob, which absorbs most of the effect of normalization (somewhat similar to using flares to distract a heat-seeking missile). Two, it uses residual connections, which helps it avoid the phenomenon where certain features are stuck at intervals of pixels. For example, the seam between two teeth may be stuck at pixels divisible by 32, because the generator learned to generate teeth during stage N-5, and consequently could only generate primitive teeth at that stage, before scaling up 5 times (thus intervals of 32). This was updated by the StyleGAN2-ADA ("ADA" stands for "adaptive"), which uses invertible data augmentation. It also tunes the amount of data augmentation applied by starting at zero, and gradually increasing it until an "overfitting heuristic" reaches a target level, thus the name "adaptive". === StyleGAN3 === StyleGAN3 improves upon StyleGAN2 by solving the "texture sticking" problem, which can be seen in the official videos. They analyzed the problem by the Nyquist–Shannon sampling theorem, and argued that the layers in the generator learned to exploit the high-frequency signal in the pixels they operate upon. To solve this, they proposed imposing strict lowpass filters between each generator's layers, so that the generator is forced to operate on the pixels in a way faithful to the continuous signals they represent, rather than operate on them as merely discrete signals. They further imposed rotational and translational invariance by using more signal filters. The resulting StyleGAN-3 is able to generate images that rotate and translate smoothly, and without texture sticking.
History of RISC OS
RISC OS, the computer operating system developed by Acorn Computers for their ARM-based Acorn Archimedes range, was originally released in 1987 as Arthur 0.20, and soon followed by Arthur 0.30, and Arthur 1.20. The next version, Arthur 2, became RISC OS 2 and was completed in September 1988 and made available in April 1989. RISC OS 3 was released with the very earliest version of the A5000 in 1991 and contained a series of new features. By 1996 RISC OS had been shipped on over 500,000 systems. RISC OS 4 was released by RISCOS Ltd (ROL) in July 1999, based on the continued development of OS 3.8. ROL had in March 1999 licensed the rights to RISC OS from Element 14 (the renamed Acorn) and eventually from the new owner, Pace Micro Technology. According to the company, over 6,400 copies of OS 4.02 on ROM were sold up until production was ceased in mid-2005. RISC OS Select was launched in May 2001 by ROL. This is a subscription scheme allowing users access to the latest OS updates. These upgrades are released as soft-loadable ROM images, separate to the ROM where the boot OS is stored, and are loaded at boot time. Select 1 was shipped in May 2002, with Select 2 following in November 2002 and the final release of Select 3 in June 2004. ROL released the ROM based OS 4.39 the same month, dubbed RISC OS Adjust as a play on the RISC OS GUI convention of calling the three mouse buttons 'Select', 'Menu' and 'Adjust'. ROL sold its 500th Adjust ROM in early 2006. RISC OS 5 was released in October 2002 on Castle Technology's Acorn clone Iyonix PC. OS 5 is a separate evolution based upon the NCOS work done by Pace for set-top boxes. In October 2006, Castle announced a source sharing license plan for elements of OS 5. This Shared Source Initiative (SSI) is managed by RISC OS Open Ltd (ROOL). RISC OS 5 has since been released under a fully free and open source Apache 2.0 license, while the older no longer maintained RISC OS 6 has not. RISC OS Six was also announced in October 2006 by ROL. This is the next generation of their stream of the operating system. The first product to be launched under the name was the continuation of the Select scheme, Select 4. A beta-version of OS 6, Preview 1 (Select 4i1), was available in 2007 as a free download to all subscribers to the Select scheme, while in April 2009 the final release of Select 5 was shipped. The latest release of RISC OS from ROL is Select 6i1, shipped in December 2009. == Arthur == The OS was designed in the United Kingdom by Acorn for the 32-bit ARM based Acorn Archimedes, and released in its first version in 1987, as the Arthur operating system. The first public release of the OS was Arthur 1.20 in June 1987. It was bundled with a desktop graphical user interface (GUI), which mostly comprises assembly language software modules, and the Desktop module itself being written in BBC BASIC. It features a colour-scheme typically described as "technicolor". The graphical desktop runs on top of a command-line driven operating system which owes much to Acorn's earlier MOS operating system for its BBC Micro range of 8-bit microcomputers. Arthur, as originally conceived, was intended to deliver similar functionality to the operating system for the BBC Master series of computers, MOS, as a reaction to the fact that a more advanced operating system research project (ARX) would not be ready in time for the Archimedes. The Arthur project team, led by Paul Fellows, was given just five months to develop it entirely from the ground up—with the directive "just make it like the BBC micro". It was intended as a stop-gap until the operating system which Acorn had under development (ARX) could be completed. However, the latter was delayed time and again, and was eventually dropped when it became apparent that the Arthur development could be extended to have a window manager and full desktop environment. Also, it was small enough to run on the first 512K machines with only a floppy disc, whereas ARX required 4 megabytes and a hard drive. The OS development was carried out using a prototype ARM-based system connected to a BBC computer, before moving onto the prototype Acorn Archimedes the A500. Arthur was not a multitasking operating system, but offered support for adding application-level cooperative multitasking. No other version of the operating system was released externally, but internally the development of the desktop and window management continued, with the addition of a cooperative multitasking system, implemented by Neil Raine, which used the memory management hardware to swap-out one task, and bring in another between call-and-return from the Wimp_Poll call that applications were obliged to make to get messages under the desktop. Reminiscent of a similar technique employed by MultiFinder on the Apple Macintosh, this transformed a single-application-at-a-time system into one that could operate a full multi-tasking desktop. This transformation took place at version 1.6 though it was not made public until released, with the name change from Arthur to RISC OS, as version 2.0. Most software made for Arthur 1.2 can be run under RISC OS 2 and later because, underneath the desktop, the original Arthur OS core, API interfaces and modular structures remain as the heart of all versions. (A few titles will not work, however, because they used undocumented features, side effects or in a few cases APIs that became deprecated). In 2011, Business Insider listed Arthur as one of ten "operating systems that time forgot". == RISC OS 2 == RISC OS was a rapid development of Arthur 1.2 after the failure of the ARX project. Given growing dissatisfaction with various bugs and limitations with Arthur, testing of what was then known as Arthur 2 was apparently ongoing during 1988 with selected software houses. At this stage, Computer Concepts, who had been prolific developers for the BBC Micro and who had begun software development for the Archimedes, had already initiated a rival operating system project, Impulse, to support their own applications (including the desktop publishing application that would eventually become Impression), stating that Arthur did not meet the "hundreds of requirements" involved including "true multi-tasking". Such an operating system was to be offered free of charge with the planned application packages, but with the release of RISC OS and Computer Concepts acknowledging that RISC OS "overcomes the old problems with Arthur", the applications were to be able to run under either RISC OS or Impulse. Impression was eventually released as a RISC OS application. Ultimately, Arthur 2 was renamed to RISC OS, and was first sold as RISC OS 2.00 in April 1989. The operating system implements co-operative multitasking with some limitations but is not multi-threaded. It uses the ADFS file system for both floppy and hard disc access. It ran from a 512 KB set of ROMs. The WIMP interface offers all the standard features and fixes many of the bugs that had hindered Arthur. It lacks virtual memory and extensive memory protection (applications are protected from each other, but many functions have to be implemented as 'modules' which have full access to the memory). At the time of release, the main advantage of the OS was its ROM; it booted very quickly and while it was easy to crash, it was impossible to permanently break the OS from software. Its high performance was due to much of the system being written in ARM assembly language. The OS was designed with users in mind, rather than OS designers. It is organised as a relatively small kernel which defines a standard software interface to which extension modules are required to conform. Much of the system's functionality is implemented in modules coded in the ROM, though these can be supplanted by more evolved versions loaded into RAM. Among the kernel facilities are a general mechanism, named the callback handler, which allows a supervisor module to perform process multiplexing. This facility is used by a module forming part of the standard editor program to provide a terminal emulator window for console applications. The same approach made it possible for advanced users to implement modules giving RISC OS the ability to do pre-emptive multitasking. A slightly updated version, RISC OS 2.01, was released later to support the ARM3 processor, larger memory capacities, and the VGA and SVGA modes provided by the Acorn Archimedes 540 and Acorn R225/R260. == RISC OS 3 == RISC OS 3 introduced a number of new features, including multitasking Filer operations, applications and fonts in ROM, no limit on number of open windows, ability to move windows off screen, safe shutdown, the Pinboard, grouping of icon bar icons, up to 128 tasks, native ability to read MS-DOS format discs and use named hard discs. Improved configuration was also included, by way of multiple windows to change the settings. RISC OS 3.00 was released with the very earliest version of the A5000 in 1991; it is almo
IBM Retail Store Systems
This article describes IBM point of sale equipment from 1973 with the introduction of the IBM 3650 till 1986 with the introduction of the IBM 4680. IBM continued to announced new retail products until the sale of the IBM Retail Store Solutions business to Toshiba TEC, announced on 17 April 17 2012. == Background == IBM began selling retail point of sale systems starting in 1973 with the IBM 3650 Retail Store System aimed at department and chain stores and the IBM 3660 Supermarket System designed for supermarkets. The IBM 3650 was announced alongside other IBM vertical industry systems such as the IBM 3600 Finance Communication System, and the IBM 3790 communications system, the combination of which IBM described as a "revolution in terminal based systems". All of these systems relied on a significant number of developments across IBM: New chips: Large Scale Integration allowed advanced Field Effect Transistor logic chips that packed far more transistors onto a new metalized one-inch square ceramic substrate Gas panels: Developed as an alternative to cathode ray tubes, the neon argon gas panel provided clear and flicker-free images. Modem communications: Synchronous Data Link Control provided lower-cost communications over telephone lines New disks: The "Gulliver" disk file that supplied a hard drive smaller than three cubic feet and also the "Igar" diskette drive Smaller printers: A disk printer system called "spica" that used a rotating disk print element with engraved print elements that are struck by a single hammer as the disk rotates Belt printers: A new system, known as "Lynx," using a removable belt that was significantly cheaper, quieter and simpler than earlier chain printers Keyboards: New keyboard technology called "Calico" that could build a wide variety of keyboards using common manufacturing facilities Power supplies: Transistorised Switching Regulators or TsRs: compact power supplies that are one third to one-fourth the size of previous generations === Store Loop (SLOOP) architecture === The 36xx retail terminals are connected to the store controller via a loop also called a Store Loop, similar to that used by the IBM 3600 Finance System. If a terminal detects an error, it runs a self-diagnosis routine, displays an error code to the operator, and uses bypass circuitry to remove itself from the loop and allow the loop to continue operating. If the loop fails, the most downstream terminal transmits an error code to the controller. Intermittent errors are written to disk on the store controller. === Supplies Manufacturing === While IBM's Data Processing Division created the retail store systems, it's Information Record Division (IRD) also saw signifiant opportunity in manufacturing supplies for retail systems. As an example in their Dayton NJ plant they used a high-speed Webtron press to create up to 1 million magnet merchandise tags per shift. == IBM 3650 Retail Store System == The 3650 System is a family of products designed to computerise a retail store, both at the point of sale and for back office store management functions. It includes a method to generate encoded tickets for merchandise, rather than use the Universal Product Code (UPC). The key devices for the system were as follows: === Shop Floor === ==== 3653 Point of Sale Terminal ==== Designed for the store floor, it is a loop attached device with: a wire matrix printer with 3 stations: cash receipt, sales-check and transaction journal. a keyboard with 10 numeric keys and 19 function keys an 8 digit display and description lights. in addition to the 8 digits it also displays the following characters: "$", "." and "-" operator guidance panel with 20 backlit captions status indicators a cash drawer a check verification station. Options include a wand magnet label reader with a 4 foot flexible cord, and locks for the journal tape and the till cover. The terminal effectively loads its software remotely from the 3651 over the loop, which IBM calls an IML (initial microcode load). It can also be IMLed locally using a tape cassette recorder. IBM later offered a choice of OEM Wand Attachments that could be ordered by RPQ that could use OCR or scan UPCs, instead of a wand magnet label reader. Only one wand could be attached to a specific 3653. There are two models: Model 1, which is not programmable. Was announced 10 August 1973. Model P1, which is customer programmable. Has 36 KB of storage expandable to 60 KB. Was announced 13 October 1978. === Back office equipment === ==== 3651 Store Controller ==== Controls data flow inside either a single store or multiple stores and sends retail transactions to a mainframe using a modem. For point of sale it performed functions such as: Automatic price lookup from a master price file Automatic distribution of net sales by up to 54 departments Automatic application of applicable discounts and sales taxes Automatic control of food stamp maximums Check authorization facilities For back office it also helped report preparation such as: store summary individual cashier performance store office reconciliation sales by up to 54 departments Current inquiries for department sales; cashier performance & cash position; store cash position. Inquiries and changes to the master price records and operator authorization control records. Setting the time and date for the internal clock. Running the customer checkouts in training mode. Printing of messages received from the host mainframe Entry of messages to send to the host mainframe Reporting of customer stock returns Updating the system with data received from the mainframe Preparing shelf Labels Basic features include: Each loop attaches up to 63 or 64 terminals depending on traffic volumes and desired response times Has an error and operator panel. There were many models including: A25 Has a 5 MB internal disk. Has 60K of memory expandable to 76KB. Supports one store loop. Attaches to 3275, 3653 and 3663. Announced 19 May 1978, withdrawn 19 February 1981 B25 Same as a A25 with a 9.2 MB internal disk. Announced 19 May 1978 C25 Announced 15 May 1981, withdrawn 15 December 1987 A50 Has a 5 MB internal disk. Announced 5 May 1975. Announced 10 August 1973, withdrawn 15 December 1987 B50 Same as B50 with a 9.2 MB internal disk. Announced 5 May 1975, withdrawn 15 December 1987 A60 Has a 5 MB internal disk. Has an integrated 3669. Attaches up to 24 3663 terminals. Announced 11 October 1973, withdrawn 15 December 1987 B60 Same as A60 with a 9.3 MB internal disk. Announced 17 November 1975, withdrawn 15 December 1987 A75 Has 5 MB internal disk. Has 60K of memory expandable to 124KB. Supports one to three store loops. Attaches to 3275, 3653, 3657, 3784 and 3663 terminals. Announced 19 May 1978 B75 Same as A75 with 9.3 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 C75 Same as A75 with 18.6 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 D75 Same as A75 with 27.9 MB internal disk. Announced 19 May 1978, withdrawn 15 December 1987 There were also two additional models that could be used instead of the 3651: 7480 Model 1: Has a 18.6 MB internal disk 7480 Model 2: Has a 27.9 MB internal disk ==== 3872 Modem ==== Used to attach to a 3659 for remote loops. Each 3872 can attach three 3659s. ==== 3659 Remote Communication Unit ==== Connected to an IBM 3872 and provides a remote loop for up to 64 point of sale terminals. Announced 10 August 1973, withdrawn 15 December 1987 (Model 2, announced 17 March 1976, withdrawn 20 December 1982) Intended to be used in a back office location like the store manager's office or the data entry office ==== 3275-3 Display Station ==== It is a loop attached display terminal with printer attachment hardware ==== 3784 Line Printer ==== A belt printer for higher-volume end-of-day reporting. The maximum print speed is 155 Ipm using a 48 character set. ==== 3657 Ticket Unit ==== Used to print tickets and encoded labels to attach to store merchandise. It is a loop attached device. It prints the following: 1" by 1" adhesive backed labels with up to 11 characters at 500 tickets per minute. IBM sold these in rolls of 9000 1" x 2" tickets with up to 42 encoded characters and two lines of print of up to 21 characters at 250 tickets per minute. IBM sold these in rolls of 2800 1" x 3" tickets with up to 79 encoded characters and two lines of print of up to 32 characters at 167 tickets per minute. IBM sold these in rolls of 1900 It can also batch read the tickets for validation, separating good tickets from bad ones into two cartridges. Announced 10 August 1973, withdrawn 15 December 1987 ==== 7481 Data Storage Unit ==== This optional unit is used to record transaction data and initialize terminals if the store controller is not available. It uses a built in tape drive to store this data. === Early deployments === The first customer installation of a 3650 was at a Dillard's department store in Little Rock, Arkansas, in late 1974. They placed arou
Bulletin (service)
Bulletin was an online newsletter platform launched by Facebook on July 6, 2021, that allows notable writers to make announcements directly to their subscribers. Its competitors included Substack, of which Bulletin was called a "near-clone." Writers participating in the platform's launch included Malcolm Gladwell, Mitch Albom, Tan France, Jessica Yellin, Jane Wells, Erin Andrews and Dorie Greenspan. Facebook CEO Mark Zuckerberg stated that Bulletin represented the first time that the company had "built a project that is directly for journalists and individual writers." In October 2022 Meta announced the shutdown of Bulletin. The platform went into read only mode in January 2023 and became unavailable in April 2023. == History == Facebook announced Bulletin as its online newsletter platform on June 29, 2021. and launched by the company on July 6, 2021. Facebook CEO Mark Zuckerberg touted the service by saying that Bulletin represented the first time that the company had "built a project that is directly for journalists and individual writers." Writers participating in the platform's launch included Malcolm Gladwell, Mitch Albom, Tan France, Jessica Yellin, Jane Wells, Erin Andrews and Dorie Greenspan. == Reception == Unlike competitor such as Substack, Facebook indicated upon service's launch that it would not take a cut of subscription fees of writers using that platform. According to Washington Post technology writer Will Oremus, the move was criticized by those who viewed it as a form of predatory pricing intended by Facebook to force those competitors out of business. Sandeep Vaheesan, legal director of the think tank Open Markets, called for the government to reexamine predatory pricing as a violation of antitrust law, saying, "We want companies to compete by making better products, investing in new equipment and tech — not purely relying on their financial advantages to capture market share."
Non-native speech database
A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems. == List == The actual table with information about the different databases is shown in Table 2. === Legend === In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is. Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues. Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used. In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.
Virtual DOM
A virtual DOM is a lightweight JavaScript representation of the Document Object Model (DOM) used in declarative web frameworks such as React, Vue.js, and Elm. Since generating a virtual DOM is relatively fast, any given framework is free to rerender the virtual DOM as many times as needed relatively cheaply. The framework can then find the differences between the previous virtual DOM and the current one (diffing), and only makes the necessary changes to the actual DOM (reconciliation). While technically slower than using just vanilla JavaScript, the pattern makes it much easier to write websites with a lot of dynamic content, since markup is directly coupled with state. Similar techniques include Ember.js' Glimmer and Angular's incremental DOM. == History == The JavaScript DOM API has historically been inconsistent across browsers, clunky to use, and difficult to scale for large projects. While libraries like jQuery aimed to improve the overall consistency and ergonomics of interacting with HTML, it too was prone to repetitive code that didn't describe the nature of the changes being made well and decoupled logic from markup. The release of AngularJS in 2010 provided a major paradigm shift in the interaction between JavaScript and HTML with the idea of dirty checking. Instead of imperatively declaring and destroying event listeners and modifying individual DOM nodes, changes in variables were tracked and sections of the DOM were invalidated and rerendered when a variable in their scope changed. This digest cycle provided a framework to write more declarative code that coupled logic and markup in a more logical way. While AngularJS aimed to provide a more declarative experience, it still required data to be explicitly bound to and watched by the DOM, and performance concerns were cited over the expensive process of dirty checking hundreds of variables. To alleviate these issues, React was the first major library to adopt a virtual DOM in 2013, which removed both the performance bottlenecks (since diffing and reconciling the DOM was relatively cheap) and the difficulty of binding data (since components were effectively just objects). Other benefits of a virtual DOM included improved security since XSS was effectively impossible and better extensibility since a component's state was entirely encapsulated. Its release also came with the advent of JSX, which further coupled HTML and JavaScript with an XML-like syntax extension. Following React's success, many other web frameworks copied the general idea of an ideal DOM representation in memory, such as Vue.js in 2014, which used a template compiler instead of JSX and had fine-grained reactivity built as part of the framework. In recent times, the virtual DOM has been criticized for being slow due to the additional time required for diffing and reconciling DOM nodes. This has led to the development of frameworks without a virtual DOM, such as Svelte, and frameworks that edit the DOM in-place such as Angular 2. == Implementations == === React === React pioneered the use of a virtual DOM to make components declaratively. Virtual DOM nodes are constructed using the createElement() function, but are often transpiled from JSX to make writing components more ergonomic. In class-based React, virtual DOM nodes are returned from the render() function, while in functional hook-based components, the return value of the function itself serves as the page markup. === Vue.js === Vue.js uses a virtual DOM to handle state changes, but is usually not directly interacted with; instead, a compiler is used to transform HTML templates into virtual DOM nodes as an implementation detail. While Vue supports writing JSX and custom render functions, it's more typical to use the template compiler since a build step isn't required that way. === Svelte === Svelte does not have a virtual DOM, with its creator Rich Harris calling the virtual DOM "pure overhead". Instead of diffing and reconciling DOM nodes at runtime, Svelte uses compile-time reactivity to analyze markup and generate JavaScript code that directly manipulates the DOM, drastically increasing performance.