AI Art Keywords

AI Art Keywords — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Watch Duty

  Watch Duty is real-time wildfire tracking and alert platform. It utilizes a combination of official data sources and human monitoring by experienced volunteers, including active and retired firefighters, dispatchers, and first responders. The service is operated by Sherwood Forestry Service, a 501(c)(3) non-profit organization. In 2025, Watch Duty had 48 full-time employees and approximately 250 volunteers who reported on over 13,000 wildfires. == History == Watch Duty was launched in August 2021 by John Mills, who experienced a wildfire shortly after he moved to Sonoma County, California. The California Department of Forestry and Fire Protection (CAL FIRE) was unable to provide updates more than once a day due to time constraints, and residents of the area were unable to monitor the progression of the wildfire. Mills discovered that updates were being shared on social media by volunteers following radio scanners, and developed the Watch Duty app to make the information more readily available. It launched with a volunteer staff of "citizen information officers," initially serving Sonoma County before expanding to all of California in June 2022. As of December 2024, the service covered 22 states west of the Mississippi River. During the January 2025 Southern California wildfires, Watch Duty was downloaded millions of times, ranking among the most popular free downloads on the iOS App Store. On December 1st, 2025, Watch Duty announced an expansion to all 50 U.S. states. == App == The application is centered around an interactive map based on OpenStreetMap data with a variety of overlays visualizing fire risk, active fires and evacuation zones, weather conditions, and air quality observations. Watch Duty sources wildfire information from radio scanner transmissions, firefighters, sheriffs, and CAL FIRE publications. It has policies against the publication of personally identifiable information, such as the names of fire victims. Watch Duty is free to use, doesn't require users to sign up, and doesn't display ads.

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  Peanut App

  Peanut, a product of Peanut App Ltd. is an online community for women who are planning to become pregnant, women who are pregnant, women who have had children, and women who are experiencing menopause. Profiles of potential friends are displayed to users who can swipe up to show intent to connect. Users can also connect via discussion threads, groups, and live audio conversations. The app allows users to select their stage of life (trying to conceive, pregnancy, motherhood, or menopause), so as to meet women at a similar life stage, and to discover relevant content. Peanut was founded by Michelle Kennedy shortly after she left Bumble, a female-first dating app. She has described Peanut as, "the app she wishes she had when she first became a mother". == History == Peanut was initially launched in 2017 for mothers and pregnant women. The app focuses on helping users find others with shared interests, such as spoken languages, occupations, and hobbies. It also displays a woman's life stage, such as the age of her children, or the stage of pregnancy. In 2018, it launched a community discussion feature that intended to give women an "alternative to other social platforms". In 2019, it started to serve women who are trying to conceive. In April 2021, it integrated live audio, in response to the COVID-19 pandemic, and the restrictions around in-person socializing. in September 2021, it started to include women who are navigating perimenopause, menopause, and postmenopausal. Although it had initially catered for younger women navigating into new families, a large number of users had undergone surgically or chemically induced menopause due to medical conditions. In July 2021, Peanut launched an investment micro fund, Peanut StartHER, focused on investing in women-owned businesses, as well as other historically excluded founders. == Operation == The Peanut app is a social network exclusively for women, focusing on topics of pregnancy, motherhood, fertility, and menopause. It is available on iOS and Android devices. Users must prove their identity, in keeping with the primary function of in-app safety, and then they can create a profile to interact with other users. For pregnant users, the “Bump Buddies” feature helps connect them with other Peanut users who have a similar due date, which aimed to help expecting mothers combat loneliness during the COVID-19 pandemic. Peanut users also have the option to join “Groups” ‒ sub-sections of users focused on specific topics, including (but not limited to) location, life stage, pregnancy due date, and interests or hobbies. The live voice chat feature “Pods”, enables Peanut users to socialize without the pressure of photos or video chat. It offers features such as a muted audience of listeners who need to virtually raise their hand to speak, emoji reactions, and hosts who can moderate the conversations and invite people to speak.

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  TargetLink

  TargetLink is a software for automatic code generation, based on a subset of Simulink/Stateflow models, produced by dSPACE GmbH. TargetLink requires an existing MATLAB/Simulink model to work on. TargetLink generates both ANSI-C and production code optimized for specific processors. It also supports the generation of AUTOSAR-compliant code for software components for the automotive sector. The management of all relevant information for code generation takes place in a central data container, called the Data Dictionary. Testing of the generated code is implemented in Simulink, which is also used for the specification of the underlying simulation models. TargetLink supports three simulation modes to test the generated code: Model-in-the-loop simulation (MIL): this mode allows the model design to be checked. An MIL simulation is also known as a floating-point simulation, since the variables are typically floating-point variables. Software-in-the-loop (SIL): the simulation is based on the execution of generated code, which runs on a PC system. The variables are typically plain or fixed point numbers. Processor-in-the-loop (PIL): in a PIL simulation, the generated code runs on the target hardware or on an evaluation board. So-called real-time frames are included, making it possible to transfer the simulation results as well as memory consumption and runtime information to the PC. The Motor Industry Software Reliability Association (MISRA) published official MISRA modeling guidelines for TargetLink in late 2007, which are particularly important for functional safety of safety-critical applications. In 2009, TÜV SÜD certified TargetLink for use during the development of safety-critical systems to ISO DIS 26262 and IEC 61508.

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  Protocol engineering

  Protocol engineering is the application of systematic methods to the development of communication protocols. It uses many of the principles of software engineering, but it is specific to the development of distributed systems. == History == When the first experimental and commercial computer networks were developed in the 1970s, the concept of protocols was not yet well developed. These were the first distributed systems. In the context of the newly adopted layered protocol architecture (see OSI model), the definition of the protocol of a specific layer should be such that any entity implementing that specification in one computer would be compatible with any other computer containing an entity implementing the same specification, and their interactions should be such that the desired communication service would be obtained. On the other hand, the protocol specification should be abstract enough to allow different choices for the implementation on different computers. It was recognized that a precise specification of the expected service provided by the given layer was important. It is important for the verification of the protocol, which should demonstrate that the communication service is provided if both protocol entities implement the protocol specification correctly. This principle was later followed during the standardization of the OSI protocol stack, in particular for the transport layer. It was also recognized that some kind of formalized protocol specification would be useful for the verification of the protocol and for developing implementations, as well as test cases for checking the conformance of an implementation against the specification. While initially mainly finite-state machine were used as (simplified) models of a protocol entity, in the 1980s three formal specification languages were standardized, two by ISO and one by ITU. The latter, called SDL, was later used in industry and has been merged with UML state machines. == Principles == The following are the most important principles for the development of protocols: Layered architecture: A protocol layer at the level n consists of two (or more) entities that have a service interface through which the service of the layer is provided to the users of the protocol, and which uses the service provided by a local entity of level (n-1). The service specification of a layer describes, in an abstract and global view, the behavior of the layer as visible at the service interfaces of the layer. The protocol specification defines the requirements that should be satisfied by each entity implementation. Protocol verification consists of showing that two (or more) entities satisfying the protocol specification will provide at their service interfaces the specified service of that layer. The (verified) protocol specification is used mainly for the following two activities: The development of an entity implementation. Note that the abstract properties of the service interface are defined by the service specification (and also used by the protocol specification), but the detailed nature of the interface can be chosen during the implementation process, separately for each entity. Test suite development for conformance testing. Protocol conformance testing checks that a given entity implementation conforms to the protocol specification. The conformance test cases are developed based on the protocol specification and are applicable to all entity implementations. Therefore standard conformance test suites have been developed for certain protocol standards. == Methods and tools == Tools for the activities of protocol verification, entity implementation and test suite development can be developed when the protocol specification is written in a formalized language which can be understood by the tool. As mentioned, formal specification languages have been proposed for protocol specification, and the first methods and tools where based on finite-state machine models. Reachability analysis was proposed to understand all possible behaviors of a distributed system, which is essential for protocol verification. This was later complemented with model checking. However, finite-state descriptions are not powerful enough to describe constraints between message parameters and the local variables in the entities. Such constraints can be described by the standardized formal specification languages mentioned above, for which powerful tools have been developed. It is in the field of protocol engineering that model-based development was used very early. These methods and tools have later been used for software engineering as well as hardware design, especially for distributed and real-time systems. On the other hand, many methods and tools developed in the more general context of software engineering can also be used of the development of protocols, for instance model checking for protocol verification, and agile methods for entity implementations. == Constructive methods for protocol design == Most protocols are designed by human intuition and discussions during the standardization process. However, some methods have been proposed for using constructive methods possibly supported by tools to automatically derive protocols that satisfy certain properties. The following are a few examples: Semi-automatic protocol synthesis: The user defines all message sending actions of the entities, and the tool derives all necessary reception actions (even if several messages are in transit). Synchronizing protocol: The state transitions of one protocol entity are given by the user, and the method derives the behavior of the other entity such that it remains in states that correspond to the former entity. Protocol derived from service specification: The service specification is given by the user and the method derives a suitable protocol for all entities. Protocol for control applications: The specification of one entity (called the plant - which must be controlled) is given, and the method derives a specification of the other entity such that certain fail states of the plant are never reached and certain given properties of the plant's service interactions are satisfied. This is a case of supervisory control. == Books == Ming T. Liu, Protocol Engineering, Advances in Computers, Volume 29, 1989, Pages 79–195. G.J. Holzmann, Design and Validation of Computer Protocols, Prentice Hall, 1991. H. König, Protocol Engineering, Springer, 2012. M. Popovic, Communication Protocol Engineering, CRC Press, 2nd Ed. 2018. P. Venkataram, S.S. Manvi, B.S. Babu, Communication Protocol Engineering, 2014.

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  SlideRocket

  SlideRocket was an online presentation platform that let users create, manage, share and measure presentations. SlideRocket was provided via a SaaS model. The company was acquired by VMware in April 2011, who sold it to ClearSlide, a similar SaaS application, in March 2013. It is no longer offering independent signups, as the platform is being integrated into ClearSlide. == History == SlideRocket was founded in Jan 2006, and launched as a private beta in March 2008 at the Under The Radar Spring event. A public beta was announced in September 2008 followed shortly by public release on October 28, 2008. SlideRocket is most commonly credited with inventing the PResuMÉ or Presentation Résumé in early 2009. On April 26, 2011, SlideRocket was acquired by VMware. On March 5, 2013, VMware sold SlideRocket to ClearSlide. SlideRocket is based in San Francisco.

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  CPT Corporation

  CPT Corporation was founded in 1971 by Dean Scheff in Minneapolis, Minnesota, with co-founders James Wienhold and Richard Eichhorn. CPT first designed, manufactured, and marketed the CPT 4200, a dual-cassette-tape machine that controlled a modified IBM Selectric typewriter to support text editing and word processing. The CPT 4200 was followed in 1976 by the CPT VM (Visual Memory), a partial-page display-screen dual-cassette-tape unit, and shortly thereafter by the CPT 8000, a full-page display dual-diskette desktop microcomputer that drove stand-alone daisy wheel printers. Subsequent products included (1) variants on the 8000 series; (2) the CPT 6000 series, which had a lower capacity, smaller screen, and was less expensive; (3) the CPT 9000 series, which had a larger capacity and could run IBM personal computer software; (4) the CPT Phoenix series, which had a graphical capabilities; (5) CPT PT, a software-only reduced version that ran on IBM personal computers and clones; and (6) other related products. The CPT logo—originally three letters chosen to sound well together—began to be taken as an acronym for "cassette powered typewriting," and subsequently for "computer processed text," and numerous other variants. Major competition was IBM, Wang, Lanier, Xerox, and other word processing vendors. CPT Corporation was fifth in size among Minnesota-based top high-tech companies, after 3M, Honeywell, Control Data, and Medtronic. Corporate revenues grew to approximately a quarter-billion dollars per year in the mid-1980s, then declined with the proliferation of personal computers. CPT ultimately ceased major manufacturing late in the 20th century. == Selected products == === Cassette based === The CPT 4200 was a dual-cassette-tape unit with a small built-in keyboard that controlled a modified IBM Selectric typewriter. Keystrokes entered on the typewriter appeared on the paper as they were recorded on the output cassette, which formed a magnetic replica of the characters printed on the page. That output cassette could later be used as an input cassette, where it would be played back to the typewriter along with new keystrokes to accomplish text editing. The keyboard of the CPT 4200 had action keys for "skip", "read" and "stop", mode keys for "word", "line", "paragraph," and "page." Pressing "read" transferred a word, line, paragraph, or page (depending on which mode key had been selected) from the input tape to both the typewriter and the output tape. Line boundaries (aka printer margins) recorded on the input tape were ignored or retained depending on whether or not the "adjust" key had been selected. Alternatively, pressing "skip" moved past the corresponding amount of text on the input tape without sending it to the typewriter or to the output tape. The Selectric's keyboard was active for any new typing, which would appear on the paper and transferred to the output tape. Thus a document was edited by reading back those parts of the text to be retained and skipping those parts to be discarded, with new typing added from the Selectric's keyboard. Price: approx. $5000, 1980-era values. The CPT Communicator was an add-on to the CPT 4200 that allowed data to be transferred from one text-editing machine to another, or between a text-editing machine and a remote computer, via phone lines. Price: not available. === Microprocessor based === ==== CPT 8000 series ==== The CPT 8000 was the company's first microcomputer product, exhibited in spring of 1976. It was a self-contained desktop machine with two 8-inch floppy diskette drives, a movable keyboard, and a full-page vertically oriented CRT display simulating paper with black characters on a white background, for a wysiwyg view of text on paper. It was promoted as familiar and easy to use for those experienced with typewriters. A keyboard with a large set of extra keys made operating the 8000 quite easy even for people without any computer skills or background. IN, OUT, PRINT, OOPS OOPS was changed thinking it was insulting to the buyer to assume they would ever make an error. The CPT 8000 was designed to show a full page of text with a static line showing the margin and tab stops. An additional line would display status or error messages with a times square like display. The times square error and status messages were very well done, "The printer needs a new ribbon" rather than "ERROR 034892". The text page could both smooth pan and scroll by the hardware in the display board and nothing quite like it existed for a very long time. The 8000 ran its own multitasking hardware interrupt-driven operating system but it also ran CP/M quite well. So unlike other companies that sold Wordprocessor only systems, CPT had a system that could run any of the many popular CP/M applications. Using the CP/M OS users could develop Fortran, CBasic, Cobol and other language's programs. The 8000 used Intel's 8080 microprocessor. The display board was bleeding-edge, high-speed logic. The parts available at this time were pushed to their limits to provide the speed needed to display this much text. There were times that batches of parts from one manufacturer simply could not be clocked as fast as the 8000 display required. Memory was initially 64K, but larger boards of 128K were most common then later 256K were offered. The 8080 accessed this additional RAM by running a custom page flipping circuit. The 8000 was originally priced at $8000 and its daisy wheel printer an additional $8000. The model number having been confused with the price at its first appearance at the Hanover fair. An RS-232 serial communication option was available for the 8000 series that allowed the electronic transfer of documents. One very popular use of this was to access the Westlaw system. A tempest approved version of the 8000 was developed that was RF tight with nothing being emitted that could be monitored or spied on. === Storage Systems === ==== CPT WordPak ==== The CPT WordPak series was CPT's first external document storage system that enabled multiple 8000 series workstations to store documents in an electronic filing cabinet. Prior to WordPak, all documents were stored on removable 8-inch floppy diskettes. Sharing documents involved handing off the original disk, or copying the document to a second disk and 'sneaker-net-ing' (walking it over) to the second 8000. But this resulted in two copies of the document, one at each workstation. A circuit board with a proprietary cable connector was installed in the 8000/6000 family of "workstations" and connected to the WordPak by a multi-conductor cable. WordPak 1 consisted of a single Shugart Associates SA4000 14"-diameter hard disk with a capacity of 30 megabytes. WordPak 2 added a 2nd drive for a total of 60 megabytes. ==== CPT SRS 45 ==== The CPT SRS 45 was what would now be called a server (quite likely the first of its kind) but in practice was much more. It was maybe the worlds easiest networking shared resource system. It combined a ZIP drive for backup and hard disk(s) that would be shared simultaneously by up to eight CPT machines that had the PC AT bus. The primary person responsible for its development was Bill Davidson whose wife Cheryl was responsible for bringing up CP/M, MP/M and other Digital Research products running on the Phoenix. The brilliance of the system were the networking cards that plugged into the individual machines. These used the 55AA installable driver of the IBM BIOS to simply add the zip and hard disk drives to each computers drives list. So a system that started with floppy drives A and B and a C hard disk on the machine would have the SRS 45 drives added as drives D (E, F depending on the number of hard disk) and Z for the zip drive. Sharing (avoiding writing to the same file at the same time) was handled by simply assigning parts of the drives for individuals and other directories for shared use. No "driver" software was needed. You simply plugged in the networking card and your machine had additional drives that were internal to the SRS45. This approach was far ahead of its time and sadly never recognized for its brilliance. The SRS45 as were all CPT machines not just dedicated Word Processors. === Personal-computer based === ==== CPT PT software ==== CPT PT was a reduced a version of the software that ran under MS-DOS as an application on IBM PC compatible computers. The corporation intended it as a bridge to allow data to flow in and out of personal computer packages, as well as providing a personal-computer word processing application for those familiar with standalone CPT equipment or who preferred the CPT style of dual-window text editing. Price: approx. $200, 1980-era values. ==== CPT Genius Display ==== The Genius display was a stand-alone, vertically-oriented (portrait) configuration monochrome grey-scale CRT monitor unit and an IBM PC form factor display card to allow high-resolution, full-page text & graphics on IBM PC compatible computers.

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  Closest point method

  The closest point method (CPM) is an embedding method for solving partial differential equations on surfaces. The closest point method uses standard numerical approaches such as finite differences, finite element or spectral methods in order to solve the embedding partial differential equation (PDE) which is equal to the original PDE on the surface. The solution is computed in a band surrounding the surface in order to be computationally efficient. In order to extend the data off the surface, the closest point method uses a closest point representation. This representation extends function values to be constant along directions normal to the surface. == Definitions == Closest Point function: Given a surface S , c p ( x ) {\displaystyle {\mathcal {S}},cp(\mathbf {x} )} refers to a (possibly non-unique) point belonging to S {\displaystyle {\mathcal {S}}} , which is closest to x {\displaystyle \mathbf {x} } [SE]. Closest point extension: Let S {\displaystyle {\mathcal {S}}} , be a smooth surface in R d {\displaystyle \mathbb {R} ^{d}} . The closest point extension of a function u : S → R {\displaystyle u:{\mathcal {S}}\rightarrow \mathbb {R} } , to a neighborhood Ω {\displaystyle \Omega } of S {\displaystyle {\mathcal {S}}} , is the function v : Ω → R {\displaystyle v:\Omega \rightarrow \mathbb {R} } , defined by v ( x ) = u ( c p ( x ) ) {\displaystyle v(\mathbf {x} )=u(cp(\mathbf {x} ))} . == Closest point method == Initialization consists of these steps [EW]: If it is not already given, a closest point representation of the surface is constructed. A computational domain is chosen. Typically this is a band around the surface. Replace surface gradients by standard gradients in R 3 {\displaystyle \mathbb {R} ^{3}} . Solution is initialized by extending the initial surface data on to the computational domain using the closest point function. After initialization, alternate between the following two steps: Using the closest point function, extend the solution off the surface to the computational domain. Compute the solution to the embedding PDE on a Cartesian mesh in the computational domain for one time step. == Banding == The surface PDE is extended into R 3 {\displaystyle \mathbb {R} ^{3}} however it is only necessary to solve this new PDE near the surface. Hence, we solve the PDE in a band surrounding the surface for efficient computational purposes. Ω c x : ‖ x − c p ( x ) ‖ 2 ≤ λ {\displaystyle \Omega _{c}{x:\|x-cp(x)\|_{2}\leq \lambda }} where λ {\displaystyle \lambda } is the bandwidth. == Example: Heat equation on a circle == Using initial profile u S ( θ , t ) = sin ⁡ ( θ ) {\displaystyle u_{S}(\theta ,t)=\sin(\theta )} leads to the solution u S ( θ , t ) = exp ⁡ ( − t ) sin ⁡ ( θ ) {\displaystyle u_{S}(\theta ,t)=\exp(-t)\sin(\theta )} for the heat equation. Forward Euler time-stepping is used with relation Δ t = 0.1 Δ x 2 {\displaystyle \Delta t=0.1\Delta x^{2}} and degree-four interpolation polynomials for the interpolations. Second-order centered differences are used for the spatial discretization. The CPM results in the expected second order error in the solution u {\displaystyle u} . == Applications == The closest point method can be applied to various PDEs on surfaces. Reaction–diffusion problems on point clouds [RD], eigenvalue problems [EV], and level set equations [LS] are a few examples.

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  Online OS

  The Online Operating System was a fully multi-lingual and free to use web desktop written in JavaScript using Ajax. It was a Windows-based desktop environment with open-source applications and system utilities developed upon the reBOX web application framework by iCUBE Network Solutions, an Austrian company located in Vienna. == About the project == OOS.cc, which is short for Online Operating System, was a web application platform that mimicked the look and feel of classic desktop operating systems such as Microsoft Windows, Mac OS X or KDE. It consisted of various open source applications built upon the so-called reBOX web application framework. As applications could be executed in an integrated and parallel way, the OOS could have been considered a web desktop or webtop. It provided basic services such as a GUI, a virtual file system, access control management and possibilities to develop and deploy applications online. As the Online Operating System was executed within a web browser, it was no real operating system but rather a portal to various web applications, offering a high usability and flexibility. The project was partly funded by grants from the Internetprivatstiftung Austria (IPA). As at 01.08.2008 almost 20.000 users have joined the oos.cc community, using the offered featured and applications. == History == The development of the web desktop was started by iCUBE Network Solutions in 2005, followed by the first beta releases in 2006. Hence, together with YouOS and eyeOS, it can be considered to be one of the first publicly available systems of its kind. The first full version including core-level multi-language support, the file system and a basic set of applications was released to the public in March 2007 on the occasion of a national exhibition (ITnT Austria Archived 2007-06-30 at the Wayback Machine) and has left beta state half a year later in October 2007. The first release considered stable (1.0.0) was published in July 2007. The project itself and the contained applications have received several national innovation awards (see,) and have gained attention mainly due to the comprehensive approach taken (see,). OOS.cc started as a national project. The full platform including all offered applications are currently available in three languages (German, English as well as Spanish) and is receiving increasing coverage around the world (for examples see, or). The current version is 1.3.01 from 01.08.2008. == Technical overview == The project is fully written in JavaScript, exclusively using DHTML techniques to run in any web browser without any additional software installation needed. The system implements a modern kind of web application model, excessively using Ajax for communicating between client components and the Java server backend in an exclusively asynchronous manner. Aim is to offer users the unique interaction behavior following the desktop metaphor, which is the main idea of any web desktop. Also typical for this sort of web application is the broadly use of Javascript-on-demand techniques, cutting the complete project source into pieces and loading them instantly when needed. Based on this technical basis, reBOX was the framework library all applications in oos.cc were built of. It is a fully flexible and extensible API, including a GUI widget set, communication mechanisms and server services offering general and framework specific services. The Online Operating System itself consisted of a basic framework, which was able to launch any JavaScript application using the reBOX library. The user interface was based on the behavior of the Windows desktop with a start menu, a task bar and a desktop background. All applications were running in this environment. At server side, there were Java based web services that ran to serve the client processes and to provide data from the relational database in the backend. oos.cc also provided an integrated development environment called Developer Suite, which allowed the community to build own applications for the desktop environment based on reBOX (see development section below). == License == All applications available in oos.cc were open source under the European Union Public Licence (EUPL). The reBOX development toolkit is free to use developing any applications for the webtop. == Features == As mentioned above, all applications published on oos.cc are open source based on the EUPL, and can be "installed" or "deinstalled" to what-ever preferences the user has. Besides global services like the multi-language support or the global theme support, as well as some minor tools and games, oos.cc offered four major services that could be used completely free of charge. Integrated and fully flexible file storage (1 GB per user) HTTP as well as FTP file transfer from and to local file system User-based file-shares within the oos-community WebDAV access Document Management (including Version Control and File Locking mechanisms) Image publishing, organization and post-processing A free sub domain (user.oos.cc) for web- or image publishing, directly integrated in the desktop Groupware applications, including free mail, fetchmail and contact management An integrated development environment where oos-applications can be created directly from within the system (see development section below) Next releases were planned to focus on an extensive security and privacy suite, dealing with challenges like anonymous communication (browsing as well as temporary mail-addresses) as well as offering encrypted password and file storage and connectivity services. Since its initial stable release, OOS.cc could have been accessed using https to ensure secure communication. == Limitations and drawbacks == Limited number of applications: no commercial applications can be hosted. Only reviewed applications are being published No processing of popular office formats (.doc, .odt, etc.) Limited language support: Only English, German and Spanish Dependence on foreign infrastructure: No possibility to extend storage, no additional/guaranteed bandwidth, etc. == Development == One of the key focuses of the team was right from the beginning to offer a very flexible and comprehensive API, that can be used to develop not only custom applications within oos.cc, but also stand-alone web-applications or to integrate single components in existing web-sites. By decoupling the development from web-related "problems" using the reBOX API web-applications can be development in a similar fashion to any Java program: Elements can be positioned and can interact like in high-level object oriented programming languages, without taking care of divs, browser specific behavior or communication handling. The framework also offers multi-language and theme support for existing as well as newly created applications, allowing changing almost every aspect of the look and feel of the used components according to the preferences of its users. For taking advantage of this approach, one of the applications offered in the OOS was an integrated Development Suite, allowing directly writing and executing code and hence creating new programs within the boundaries of the web computer. All applications on oos.cc were released as open source, thus all existing programs were offered to be imported, reviewed or changed and then locally deployed. Following this idea, every user was free to submit changed or newly created applications to be included in the globally offered application set. The last release offered features like auto-completion and an outline-window.

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  Tuber (app)

  Tuber (Chinese: Tuber浏览器) was a web browser mobile app developed by Shanghai Fengxuan Information Technology that allowed users within mainland China to view filtered versions of certain websites normally blocked by the Great Firewall. Filtered versions of websites such as Google, Facebook, Instagram, YouTube, Twitter, Netflix, IMDb, and Wikipedia could be viewed. The app was backed by cybersecurity company Qihoo 360 which served as the parent company. The app required phone number registration. Sensitive keywords were blocked by the app. On October 9, 2020, Global Times editor Rita Bai Yunyi tweeted that the move represented "a great step for China's opening up". The app was removed from China domestic app stores and operations ceased as of October 10, 2020. On October 12, when questioned by a Bloomberg News reporter on the topic, Foreign Ministry spokesperson Zhao Lijian replied, "This is not a diplomatic issue, and I do not have the relevant information you mentioned. China has always managed the Internet in accordance with the law. I suggest you ask the competent department for the specific situation."

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  Limnu

  Limnu was an online whiteboarding app founded in 2015 by David DeBry and David Hart. It allowed users to draw on virtual whiteboards and invite others by e-mail or by sharing a link. Invitees see any changes to the board in real time and, if allowed by the owner of the board, can also draw on the board. The service was accessible through a web application in desktop and mobile web browsers, as well as through an iOS application. It was headquartered in San Mateo, California. == History == In 2018, ZipSocket, a maker of online meeting software acquired Limnu. == Staff Directory == Andrew Kunz - CEO & Founder of ZipSocket Jenny Rice - Product Manager Max Requenes - Software Engineer Henry Maguire - Machine Learning Engineer

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  Distributed file system for cloud

  A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations (create, delete, modify, read, write) on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system. Users can share computing resources through the Internet thanks to cloud computing which is typically characterized by scalable and elastic resources – such as physical servers, applications and any services that are virtualized and allocated dynamically. Synchronization is required to make sure that all devices are up-to-date. Distributed file systems enable many big, medium, and small enterprises to store and access their remote data as they do local data, facilitating the use of variable resources. == Overview == === History === Today, there are many implementations of distributed file systems. The first file servers were developed by researchers in the 1970s. Sun Microsystem's Network File System became available in the 1980s. Before that, people who wanted to share files used the sneakernet method, physically transporting files on storage media from place to place. Once computer networks started to proliferate, it became obvious that the existing file systems had many limitations and were unsuitable for multi-user environments. Users initially used FTP to share files. FTP first ran on the PDP-10 at the end of 1973. Even with FTP, files needed to be copied from the source computer onto a server and then from the server onto the destination computer. Users were required to know the physical addresses of all computers involved with the file sharing. === Supporting techniques === Modern data centers must support large, heterogenous environments, consisting of large numbers of computers of varying capacities. Cloud computing coordinates the operation of all such systems, with techniques such as data center networking (DCN), the MapReduce framework, which supports data-intensive computing applications in parallel and distributed systems, and virtualization techniques that provide dynamic resource allocation, allowing multiple operating systems to coexist on the same physical server. === Applications === Cloud computing provides large-scale computing thanks to its ability to provide the needed CPU and storage resources to the user with complete transparency. This makes cloud computing particularly suited to support different types of applications that require large-scale distributed processing. This data-intensive computing needs a high performance file system that can share data between virtual machines (VM). Cloud computing dynamically allocates the needed resources, releasing them once a task is finished, requiring users to pay only for needed services, often via a service-level agreement. Cloud computing and cluster computing paradigms are becoming increasingly important to industrial data processing and scientific applications such as astronomy and physics, which frequently require the availability of large numbers of computers to carry out experiments. == Architectures == Most distributed file systems are built on the client-server architecture, but other, decentralized, solutions exist as well. === Client-server architecture === Network File System (NFS) uses a client-server architecture, which allows sharing of files between a number of machines on a network as if they were located locally, providing a standardized view. The NFS protocol allows heterogeneous clients' processes, probably running on different machines and under different operating systems, to access files on a distant server, ignoring the actual location of files. Relying on a single server results in the NFS protocol suffering from potentially low availability and poor scalability. Using multiple servers does not solve the availability problem since each server is working independently. The model of NFS is a remote file service. This model is also called the remote access model, which is in contrast with the upload/download model: Remote access model: Provides transparency, the client has access to a file. He sends requests to the remote file (while the file remains on the server). Upload/download model: The client can access the file only locally. It means that the client has to download the file, make modifications, and upload it again, to be used by others' clients. The file system used by NFS is almost the same as the one used by Unix systems. Files are hierarchically organized into a naming graph in which directories and files are represented by nodes. === Cluster-based architectures === A cluster-based architecture ameliorates some of the issues in client-server architectures, improving the execution of applications in parallel. The technique used here is file-striping: a file is split into multiple chunks, which are "striped" across several storage servers. The goal is to allow access to different parts of a file in parallel. If the application does not benefit from this technique, then it would be more convenient to store different files on different servers. However, when it comes to organizing a distributed file system for large data centers, such as Amazon and Google, that offer services to web clients allowing multiple operations (reading, updating, deleting,...) to a large number of files distributed among a large number of computers, then cluster-based solutions become more beneficial. Note that having a large number of computers may mean more hardware failures. Two of the most widely used distributed file systems (DFS) of this type are the Google File System (GFS) and the Hadoop Distributed File System (HDFS). The file systems of both are implemented by user level processes running on top of a standard operating system (Linux in the case of GFS). ==== Design principles ==== ===== Goals ===== Google File System (GFS) and Hadoop Distributed File System (HDFS) are specifically built for handling batch processing on very large data sets. For that, the following hypotheses must be taken into account: High availability: the cluster can contain thousands of file servers and some of them can be down at any time A server belongs to a rack, a room, a data center, a country, and a continent, in order to precisely identify its geographical location The size of a file can vary from many gigabytes to many terabytes. The file system should be able to support a massive number of files The need to support append operations and allow file contents to be visible even while a file is being written Communication is reliable among working machines: TCP/IP is used with a remote procedure call RPC communication abstraction. TCP allows the client to know almost immediately when there is a problem and a need to make a new connection. ===== Load balancing ===== Load balancing is essential for efficient operation in distributed environments. It means distributing work among different servers, fairly, in order to get more work done in the same amount of time and to serve clients faster. In a system containing N chunkservers in a cloud (N being 1000, 10000, or more), where a certain number of files are stored, each file is split into several parts or chunks of fixed size (for example, 64 megabytes), the load of each chunkserver being proportional to the number of chunks hosted by the server. In a load-balanced cloud, resources can be efficiently used while maximizing the performance of MapReduce-based applications. ===== Load rebalancing ===== In a cloud computing environment, failure is the norm, and chunkservers may be upgraded, replaced, and added to the system. Files can also be dynamically created, deleted, and appended. That leads to load imbalance in a distributed file system, meaning that the file chunks are not distributed equitably between the servers. Distributed file systems in clouds such as GFS and HDFS rely on central or master servers or nodes (Master for GFS and NameNode for HDFS) to manage the metadata and the load balancing. The master rebalances replicas periodically: data must be moved from one DataNode/chunkserver to another if free space on the first server falls below a certain threshold. However, this centralized approach can become a bottleneck for those master servers, if they become unable to manage a large number of file accesses, as it increases their already heavy loads. The load rebalance problem is NP-hard. In order to get a large number of chunkservers to work in collaboration, and to

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  Scripped

  Scripped was an online screenplay services company offering three services: script writing, script registration, and script coverage. Scripped did not facilitate collaboration among screenwriters. It combined with Zhura in 2010. According to Techcrunch, Scripped had more than 60,000 writers as of March 2010. Scripped was administered by Sunil Rajaraman, Ryan Buckley and Zak Freer. Actor, writer, and director Edward Burns and screenwriter Steven E. de Souza joined Scripped's Board of Advisers in May 2008. In 2008, the company formed a partnership with Write Brothers, makers of Movie Magic Screenwriter software. On March 29, 2010, Scripped announced that it closed $250,000 in private investment and merged with competitor Zhura. Scripped's CEO, Sunil Rajaraman, remains the merged company's Chief Executive Officer. On April 1, 2015, citing a serious technical failure, Scripped shuttered its service. As part of the announcement, it was disclosed that their backup servers had failed as well, losing all of its users' stored scripts. The website URL currently redirects to WriterDuet's website, another online scriptwriting service; Scripped had advertised WriterDuet in Scripped's shutdown open letter. == Features == The Scripped Writer provided a built-in screenplay template which formatted the document to a standard for scripts as recommended by the AMPAS. The screenplay document was composed of seven elements: scene, action, character, dialog, parenthetical, transition and general. Each element had a specific style to which the Scripped Writer conformed as text was entered. Like other client-side screenplay software, Scripped offered Tab-Enter toggling between screenplay elements, making the writing process much faster. Text files could be imported into the Scripped Writer and automatically conformed to the screenplay template. Completed scripts could be exported as PDF files. In May 2011 the administrators of Scripped launched Scripted.com - a sister site focused on freelance writing jobs. Subsequent to the service's launch, the company was renamed to Scripted, Inc.

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  Averbis

  Averbis has a focus on healthcare, pharma, automotive and intellectual property analytics. Averbis is involved in various research projects of the German Federal Ministry of Economics and Energy and the European Union such as DebugIT, EUCases, Mantra and SEMCARE. In addition to these projects, Averbis was also involved in the following projects: Greenpilot is a virtual library, which provides technical information in the fields of nutrition, environment and agriculture. Medpilot is a virtual library, which provides information about medicine and related sciences. In 2013, Averbis has been nominated for the German Founder Prize 2013. Averbis GmbH provides text analytics and text mining software to transform unstructured text into actionable information. It was founded in 2007 by IT experts after years of relevant scientific experience in the field of text mining and multilingual information retrieval. Averbis works in the field of terminology management, natural language processing, machine learning and semantic search. Its text mining software is embedded into the text mining framework UIMA.

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  Oracle Cloud Platform

  Oracle Cloud Platform refers to a Platform as a Service (PaaS) offerings by Oracle Corporation as part of Oracle Cloud Infrastructure. These offerings are used to build, deploy, integrate and extend applications in the cloud. The offerings support a variety of programming languages, databases, tools and frameworks including Oracle-specific, open source and third-party software and systems. == Deployment models == Oracle Cloud Platform offers public, private and hybrid cloud deployment models. == Architecture == Oracle Cloud Platform provides both Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). The infrastructure is offered through a global network of Oracle managed data centers. Oracle deploys their cloud in Regions. Inside each Region are at least three fault-independent Availability Domains. Each of these Availability Domains contains an independent data center with power, thermal and network isolation. Oracle Cloud is generally available in North America, EMEA, APAC and Japan with announced South America and US Govt. regions coming soon.

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  Differentiable imaging

  Differentiable imaging is a method within computational imaging that incorporates differentiable programming to design imaging systems. It treats the entire imaging process - from light passing through optical components to the numerical reconstruction—as a differentiable programming problem. This approach links optical hardware with numerical reconstruction, enabling joint optimization of both parts through differentiable programming. Differentiable imaging additionally extends the scope of computational imaging beyond image reconstruction, such as by aiding in characterization of optical components. == Background == Computational imaging combines optical hardware and computational algorithms to capture and reconstruct information that conventional imaging system cannot. This is achieved from a combination of the imaging system and the software used in the image reconstruction. Since the captured information may not directly show the image of the target, these systems often rely on numerical models that describe how light encodes the target. In practice, such models may deviate from the physical systems due to uncertainties such as noise, misalignments, manufacturing imperfections, environmental variations, etc. These uncertainties can cause a mismatch between the physical system and its numerical model, which may degrade reconstruction quality and limit the effectiveness of the hardware–software co-design. Uncertainty quantification is also studied in other hybrid physical–numerical systems, such as digital twin. While numerical modeling imaging systems date back to the several decades, such as the multislice method in electron microscopy or X-Ray nanotomography, differentiable imaging emphasizes jointly modeling uncertainties and solving inverse problems with image reconstruction simultaneously. Differentiable imaging transforms the traditional encoding model y = f ( x ) {\textstyle y=f(x)} into a more comprehensive formulation y = f ( x , θ ) {\textstyle y=f(x,\theta )} , where θ {\displaystyle \theta } represents a parameter set of mismatches between physical systems and numerical models. The forward model captures the entire imaging pipeline through a series of interconnected component functions: y = f ( x , θ ) , f = f n o i s e ∘ f c ∘ f o c ∘ f x ∘ f o i ∘ f i , {\displaystyle y=f(x,\theta ),\qquad f=f_{noise}\circ f_{c}\circ f_{oc}\circ f_{x}\circ f_{oi}\circ f_{i},} where the function composition operator ∘ {\displaystyle \circ } connects each system component, and θ = { θ c , θ o c , … } {\displaystyle \theta =\{\theta _{c},\theta _{oc},\ldots \}} encompasses uncertainty system parameters. Each component corresponds to specific physical processes within the imaging system, from illumination through object interactions to sensor behavior and noises. This forward model enables the formulation of an inverse problem that simultaneously optimizes system parameters while reconstructing images: x ∗ , θ ∗ = argmin x , θ L ( f ( x , θ ) , y ) + ∑ n = 1 N β n R n ( x ) {\displaystyle x^{},\theta ^{}={\text{argmin}}_{x,\theta }{\mathcal {L}}(f(x,\theta ),y)+\sum _{n=1}^{N}\beta _{n}{\mathcal {R}}_{n}(x)} s . t . x ∈ Ω x , θ ∈ Ω θ {\displaystyle s.t.\quad x\in \Omega _{x},\theta \in \Omega _{\theta }} Here, L ( f ( x , θ ) , y ) {\displaystyle {\mathcal {L}}(f(x,\theta ),y)} represents the fidelity term that quantifies the discrepancy between the model predictions and measured data. The whole process of the y = f ( x , θ ) {\displaystyle y=f(x,\theta )} is constructed as a computer graph based on differentiable programming, and the inverse problem is solved with gradient based algorithm, while the gradient is calculated with automatic differentiation. == Applications == One application of differentiable imaging is uncertainty management, which seeks to quantify and mitigate the impact of factors induce reality-numerical mismatch. Explicitly accounting for uncertainties can improve reconstruction accuracy and system robustness. Examples include: Model-related uncertainties: unknown or unmeasurable variables—for instance, optical system quantities that differ from the design specifications Data and system uncertainties: artifacts introduced during image acquisition, such as low-quality data, noise, or hardware imperfections Manufacturing uncertainties: variability in the production of imaging hardware—such as slight deviations in lens curvature or sensor alignment—that alters the physical system's behavior

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