AI Coding On Mobile

AI Coding On Mobile — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Game Jolt

Game Jolt is a social community platform for video games, gamers and content creators. Founded by Yaprak and David DeCarmine, it is available on iOS, Android, and on the web and as a desktop app for Windows and Linux. Users share interactive content through a variety of formats including images, videos, live streams, chat rooms, and virtual events. == Features == === Crowd streaming === In 2021 Game Jolt revealed their own live streaming feature called Firesides. Firesides allowed multiple users to simultaneously livestream together with nearly no delay. The feature launched with a virtual concert showcasing its ability to accommodate multiple streamers. On October 16, 2023, Firesides were removed from Game Jolt. === Mobile app === Game Jolt Social by Game Jolt Inc. launched on both the Apple App Store and Google Play Store in March 2022. "It's clear to us that Gen Z is tired of generic social media and they want a place specifically for gaming that supports all types of content they're creating–art, videos, thoughts, and livestreams all in one place." said Game Jolt founder and CEO Yaprak DeCarmine, in a statement to VentureBeat. === Game API === The Game Jolt Application Programming Interface (usually known as the Game Jolt Game API) allows any developer using a game development platform that supports HTTP operations and MD5 or SHA-1. Game Jolt advertises that the API can: Create multiple "scoreboards" which collect high scores from players made publicly available on the game's profile and give user accounts EXP Award player's trophies which give user accounts EXP Store game data on Game Jolt's data servers Log whether a user is currently playing a game they're logged into via the GJAPI == Game jams and competitions == Game Jolt regularly hosts game jams where participants are encouraged to develop games for a chance to win prizes. They hosted their first game jam in 2009, Shocking Contest. In November 2014, Game Jolt announced the "Indies vs PewDiePie" game jam, partnering with the popular YouTuber Felix "PewDiePie" Kjellberg. Developers were given a weekend (21–24 November) to create a game with the theme of "fun to play, fun to watch" to suit the Let's Plays entertainment style. Users could rate entries afterwards until December 1 when the scores were counted up. The prize to the top 10 rated games was Felix playing the games on his channel as a means of promotion for the developers, although later he played other entries. One of the participants of the jam, now known as Outerminds Inc. was discovered and hired by PewDiePie to develop his mobile game, Legend of the Brofist. Game Jolt partnered with Felix, Sean "Jacksepticeye" McLoughlin and Mark "Markiplier" Fischbach to host "Indies vs Gamers" in July 2015. The requirements for entries were arcade games using the Game Jolt Game API highscore tables, to be made between the July 17–20 and the top 5 games were played on the partner's YouTube channels. Following the "Indies vs PewDiePie" game jam in 2014, Game Jolt released their internal jam hosting tools public for all users to use as a service, to create their own game jams that integrated with the main site. Today, Game Jolt focuses on hosting and co-hosting game competitions with established brands in order to bring monetary and educational opportunities to their users. On April 15, 2024, an announcement was made about a collaboration with Pocket Worlds for the "HighRise Game Jam". Pocket Worlds had sold NFTs up until roughly 2022, causing a community outburst. The situation was addressed, and the situation started to disperse. == Contests == == Events == Game Jolt hosts both physical and virtual events to entertain and prank its users, which consists of the following: == History == Game Jolt has supported independent creators with a central platform to manage their content and communities since its start in 2003. David DeCarmine began development of Game Jolt at the age of 14 for a group of hobbyists, making games and sharing on forums in an early iteration known as Holo World. The original intention was to create a platform for gamers where new games could be discoverable and quickly playable, and where feedback could be provided directly to the creators, allowing them to continue improving their games. In 2008, Game Jolt was registered as an LLC, then incorporated as Game Jolt Inc. in September 2020. A new site launched in 2015 featuring a responsive design, automated curation for both games and game news articles which weighs how recent a game was uploaded and how popular it is ("hot") and filtering options on game listings for platform, maturity rating and development status. In March 2022, Game Jolt launched a mobile application simultaneously on the Google Play Store and Apple App Store targeted at Gen Z gamers and creators. While in beta, the mobile app had 100,000 installs pre-launch. === Game store === Game Jolt continues to host a large library of independent games. Game developers can upload their games directly to the site to share or sell. They would allow distribution for downloadable games, later adding support for Adobe Flash, Unity and Java games which allowed support for browser based games. In February 2013, Game Jolt built support for browser-based HTML5 games as well. A user levelling system was released into public beta in April 2013, incorporating the GJAPI trophies and highscores, as well as site activity, to generate 'EXP' (experience points). Game Jolt Jams released in early 2014 as a service to allow users to create their own game jams that integrated with the main site. In April 2016, an online marketplace was announced and released the following month with an exclusive set of game titles, including Bendy and the Ink Machine, allowing developers to sell their games on the site. In January 2016, Game Jolt released source code of the client and site's front end on GitHub under MIT license. In January 2022, Game Jolt banned adult games from appearing on the site, stating in an email to developers that the site had become a "social media platform" and they "had to make decisions around the direction and future of the brand which has now included the removal of hosted games with explicitly adult content." In response to a tweet by Itch.io saying the site is not for prudes, they wrote in their own tweet: "Game Jolt is a platform with a large audience of 13-16 year olds. Our users asked us to clean up, so here we are." == Investments == After bootstrapping Game Jolt with revenue earned from ads on the website for years, the DeCarmines secured venture capital in 2020 from SoftBank, doing so again in 2021 from founders of Twitch, Rec Room, Modio and more.
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Online analytical processing

In computing, online analytical processing (OLAP) (), is an approach to quickly answer multi-dimensional analytical (MDA) queries. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing. Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the OLAP cube and view (dicing) the slices from different viewpoints. These viewpoints are sometimes called dimensions (such as looking at the same sales by salesperson, or by date, or by customer, or by product, or by region, etc.). Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time. They borrow aspects of navigational databases, hierarchical databases and relational databases. OLAP is typically contrasted to OLTP (online transaction processing), which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. Whereas OLAP systems are mostly optimized for read, OLTP has to process all kinds of queries (read, insert, update and delete). == Overview of OLAP systems == At the core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube). It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging. The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure. A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale. For example: Sales Fact Table +-------------+----------+ | sale_amount | time_id | +-------------+----------+ Time Dimension | 930.10| 1234 |----+ +---------+-------------------+ +-------------+----------+ | | time_id | timestamp | | +---------+-------------------+ +---->| 1234 | 20080902 12:35:43 | +---------+-------------------+ === Multidimensional databases === Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube. "Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions". Even when data is manipulated it remains easy to access and continues to constitute a compact database format. The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications. Analytical databases use these databases because of their ability to deliver answers to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. === Aggregations === It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions, using an aggregate function (or aggregation function). The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data. Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A search algorithm. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently. For example, the overall sum of a roll-up is just the sum of the sub-sums in each cell. Functions that can be decomposed in this way are called decomposable aggregation functions, and include COUNT, MAX, MIN, and SUM, which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions. In other cases, the aggregate function can be computed by computing auxiliary numbers for cells, aggregating these auxiliary numbers, and finally computing the overall number at the end; examples include AVERAGE (tracking sum and count, dividing at the end) and RANGE (tracking max and min, subtracting at the end). In other cases, the aggregate function cannot be computed without analyzing the entire set at once, though in some cases approximations can be computed; examples include DISTINCT COUNT, MEDIAN, and MODE; for example, the median of a set is not the median of medians of subsets. These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online (slow) or precomputing them for possible rollouts (large space). == Types == OLAP systems have been traditionally categorized using the following taxonomy. === Multidimensional OLAP (MOLAP) === MOLAP (multi-dimensional online analytical processing) is the classic form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing. Such MOLAP tools generally utilize a pre-calculated data set referred to as a data cube. The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. Advantages of MOLAP Fast query performance due to optimized storage, multidimensional indexing and caching. Smaller on-disk size of data compared to data stored in relational database due to compression techniques. Automated computation of higher-level aggregates of the data. It is very compact for low dimension data se
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Grid-oriented storage

Grid-oriented Storage (GOS) was a term used for data storage by a university project during the era when the term grid computing was popular. == Description == GOS was a successor of the term network-attached storage (NAS). GOS systems contained hard disks, often RAIDs (redundant arrays of independent disks), like traditional file servers. GOS was designed to deal with long-distance, cross-domain and single-image file operations, which is typical in Grid environments. GOS behaves like a file server via the file-based GOS-FS protocol to any entity on the grid. Similar to GridFTP, GOS-FS integrates a parallel stream engine and Grid Security Infrastructure (GSI). Conforming to the universal VFS (Virtual Filesystem Switch), GOS-FS can be pervasively used as an underlying platform to best utilize the increased transfer bandwidth and accelerate the NFS/CIFS-based applications. GOS can also run over SCSI, Fibre Channel or iSCSI, which does not affect the acceleration performance, offering both file level protocols and block level protocols for storage area network (SAN) from the same system. In a grid infrastructure, resources may be geographically distant from each other, produced by differing manufacturers, and have differing access control policies. This makes access to grid resources dynamic and conditional upon local constraints. Centralized management techniques for these resources are limited in their scalability both in terms of execution efficiency and fault tolerance. Provision of services across such platforms requires a distributed resource management mechanism and the peer-to-peer clustered GOS appliances allow a single storage image to continue to expand, even if a single GOS appliance reaches its capacity limitations. The cluster shares a common, aggregate presentation of the data stored on all participating GOS appliances. Each GOS appliance manages its own internal storage space. The major benefit of this aggregation is that clustered GOS storage can be accessed by users as a single mount point. GOS products fit the thin-server categorization. Compared with traditional “fat server”-based storage architectures, thin-server GOS appliances deliver numerous advantages, such as the alleviation of potential network/grid bottle-necks, CPU and OS optimized for I/O only, ease of installation, remote management and minimal maintenance, low cost and Plug and Play, etc. Examples of similar innovations include NAS, printers, fax machines, routers and switches. An Apache server has been installed in the GOS operating system, ensuring an HTTPS-based communication between the GOS server and an administrator via a Web browser. Remote management and monitoring makes it easy to set up, manage, and monitor GOS systems. == History == Frank Zhigang Wang and Na Helian proposed a funding proposal to the UK government titled “Grid-Oriented Storage (GOS): Next Generation Data Storage System Architecture for the Grid Computing Era” in 2003. The proposal was approved and granted one million pounds in 2004. The first prototype was constructed in 2005 at Centre for Grid Computing, Cambridge-Cranfield High Performance Computing Facility. The first conference presentation was at IEEE Symposium on Cluster Computing and Grid (CCGrid), 9–12 May 2005, Cardiff, UK. As one of the five best work-in-progress, it was included in the IEEE Distributed Systems Online. In 2006, the GOS architecture and its implementations was published in IEEE Transactions on Computers, titled “Grid-oriented Storage: A Single-Image, Cross-Domain, High-Bandwidth Architecture”. Starting in January 2007, demonstrations were presented at Princeton University, Cambridge University Computer Lab and others. By 2013, the Cranfield Centre still used future tense for the project. Peer-to-peer file sharings use similar techniques.
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Information and media literacy

Information and media literacy (IML) is a combination of information literacy and media literacy. It enables people to show and make informed judgments as users of information and media, as well as to become skillful creators and producers of information and media messages. The transformative nature of IML includes creative works and creating new knowledge; to publish and collaborate responsibly requires ethical, cultural and social understanding. IML is also known as media and information literacy (MIL). UNESCO first adopted the term MIL in 2008 as a "composite concept" combining the competencies of information literacy and media literacy. UNESCO emphasizes the importance of global education in media and information literacy, and in 2013 defined Media and Information Literacy (MIL) as the ability to access, evaluate, use, and create information and media content in critical and ethical ways. Prior to the 1990s, the primary focus of information literacy was research skills. Media literacy, a study that emerged around the 1970s, traditionally focuses on the analysis and the delivery of information through various forms of media. Information literacy, as a skill proposed as early as 1974, centers on an individual's ability to recognize information needs and effectively locate, evaluate, and use information. These days, the study of information literacy has been extended to include the study of media literacy in many countries like the UK, Australia and New Zealand. It is also referred to as information and communication technologies (ICT) in the United States. Educators such as Gregory Ulmer have also defined the field as electracy.Media literacy is the ability to actively inquire into and think critically about information. It includes the ability to understand, evaluate, and create media content, and is an essential skill in today's information society. Livingstone, Van Couvering, and Thumim (2008) described the distinction between media literacy and information literacy: "Media literacy views media as lenses or windows for observing the world and expressing the self, whereas information literacy sees information as a tool for taking action in the world." == Integration of media and information literacy == Historically, the fields of information and media literacy have been separate, but over the course of the 21st century there have been calls to integrate both fields. Most definitions of information and media literacy include not only the abilities to locate, access, and analyze information but also the ability to create information. Only by integrating media literacy with information literacy can students better understand the sources of information and how it is used. Media education has primarily taken place in educational institutions, while information education has primarily occurred in libraries. Discussions surrounding the overlap of information literacy and media literacy came to fruition in the mid-to-late 2000s and 2010s as noted by Marcus Leaning. == In the digital age == The definition of literacy is "the ability to read and write". In practice many more skills are needed to locate, critically assess and make effective use of information. By extension, literacy now also includes the ability to manage and interact with digital information and media, in personal, shared and public domains. Historically, "information literacy" has largely been seen from the relatively top-down, organisational viewpoint of library and information sciences. However the same term is also used to describe a generic "information literacy" skill. The modern digital age has led to the proliferation of information spread across the Internet. Individuals must be able to recognize whether information is true or false and better yet know how to locate, evaluate, use, and communicate information in various formats; this is called information literacy. Towards the end of the 20th century, literacy was redefined to include "new literacies" relating to the new skills needed in everyday experience. "Multiliteracies" recognised the multiplicity of literacies, which were often used in combination. "21st century skills" frameworks link new literacies to wider life skills such as creativity, critical thinking, accountability. What these approaches have in common is a focus on the multiple skills needed by individuals to navigate changing personal, professional and public "information landscapes". As the conventional definition of literacy itself continues to evolve among practitioners, so too has the definition of information literacies. Noteworthy definitions include: Zurkowski defined information literacy as "the ability to find known or knowable content on any subject." CILIP, the Chartered Institute of Library and Information Practitioners, defines information literacy as "the ability to think critically and make balanced judgements about any information we find and use". In the United States, the definition proposed by the Association of College and Research Libraries (ACRL) is the most widely recognized. It defines information literacy as "a set of abilities requiring individuals to recognize when information is needed and to locate, evaluate, and use the needed information effectively." JISC, the Joint Information Systems Committee, refers to information literacy as one of six "digital capabilities", seen as an interconnected group of elements centered on "ICT literacy". Mozilla groups digital and other literacies as "21st century skills", a "broad set of knowledge, skills, habits and traits that are important to succeed in today's world". UNESCO, the United Nations Educational, Scientific and Cultural Organization, recognizing the necessity of teaching and learning both traditional and new types of information, the global importance of education was emphasized in 2008 through the "Teacher Media and Information Literacy (MIL) Curriculum". It defines MIL as a set of competencies that enable citizens to access, retrieve, understand, evaluate, use, create, and share information and media content in all formats through various tools in a critical, ethical, and effective manner, so as to participate in and carry out personal, professional, and social activities. Besides this, UNESCO also asserts information literacy as a "universal human right". == 21st-century students == In modern society, although the overall level of education has improved, the channels for knowledge production and dissemination have become increasingly diverse and commercialized, and traditional authoritative institutions no longer hold a monopoly over knowledge validation. While digital platforms have broadened access to information, they have also weakened trust mechanisms and evaluation standards, making epistemological skepticism a norm. Moreover, with the rise and spread of social media, misinformation and disinformation can be just as easily accessed in both densely and sparsely populated areas. These factors further underscore the importance of information literacy education. The IML learning capacities prepare students to be 21st century literate. According to Jeff Wilhelm (2000), "technology has everything to do with literacy. And being able to use the latest electronic technologies has everything to do with being literate." He supports his argument with J. David Bolter's statement that "if our students are not reading and composing with various electronic technologies, then they are illiterate. They are not just unprepared for the future; they are illiterate right now, in our current time and context". In a broader sense, developing this advanced competency of media and information literacy is essential, as it is crucial for students to exercise their freedom of expression in the 21st century. Wilhelm's statement is supported by the 2005 Wired World Phase II (YCWW II) survey conducted by the Media Awareness Network of Canada on 5000 Grade 4 – 11 students. The key findings of the survey were: 62% of Grade 4 students prefer the Internet. 38% of Grade 4 students prefer the library. 91% of Grade 11 students prefer the Internet. 9% of Grade 11 students prefer the library. Marc Prensky (2001) uses the term "digital native" to describe people who have been brought up in a digital world. The Internet has been a pervasive element of young people's home lives. 94% of kids reported that they had Internet access at home, and a significant majority (61%) had a high-speed connection. By the time kids reach Grade 11, half of them (51 percent) have their own Internet-connected computer, separate and apart from the family computer. The survey also showed that young Canadians are now among the most wired in the world. Contrary to the earlier stereotype of the isolated and awkward computer nerd, today's wired kid is a social kid. In general, many students are better networked through the use of technology than most teachers and parents, who may not understand the abilities of technology.
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Meta AI

Meta AI is a research division of Meta (formerly Facebook) that develops artificial intelligence and augmented reality technologies. == History == Meta AI was founded in 2013 as Facebook Artificial Intelligence Research (FAIR). It has workspaces in Menlo Park, London, New York City, Paris, Seattle, Pittsburgh, Tel Aviv, and Montreal as of 2025. In 2016, FAIR partnered with Google, Amazon, IBM, and Microsoft in creating the Partnership on Artificial Intelligence to Benefit People and Society. Meta AI was directed by Yann LeCun until 2018, when Jérôme Pesenti succeeded the role. Pesenti is formerly the CTO of IBM's big data group. FAIR's research includes self-supervised learning, generative adversarial networks, document classification and translation, and computer vision. FAIR released Torch deep-learning modules as well as PyTorch in 2017, an open-source machine learning framework, which was subsequently used in several deep learning technologies, such as Tesla's autopilot and Uber's Pyro. That same year, a pair of chatbots were falsely rumored to be discontinued for developing a language that was unintelligible to humans. FAIR clarified that the research had been shut down because they had accomplished their initial goal to understand how languages are generated by their models, rather than out of fear. FAIR was renamed Meta AI following the rebranding that changed Facebook, Inc. to Meta Platforms Inc. On October 1, 2025, Facebook announced "We will soon use your interactions with AI at Meta to personalize the content and ads you see". == Virtual assistant == Meta AI is also the name of the virtual assistant developed by the team, now integrated as a chatbot into Meta's social networking products. It is also available as a subscription-based stand-alone app. The virtual assistant was pre-installed on the second generation of Ray-Ban Meta smartglasses, and can incorporate inputs from the glasses' cameras after an update. It is also available on Quest 2 and newer HMDs. Since May 2024, the chatbot has summarized news from various outlets without linking directly to original articles, including in Canada, where news links are banned on its platforms. This use of news content without compensation and attribution has raised ethical and legal concerns, especially as Meta continues to reduce news visibility on its platforms. == Current research == === Natural language processing and chatbot === Natural language processing is the ability for machines to understand and generate natural language. The team is also researching unsupervised machine translation and multilingual chatbots. ==== Galactica ==== Galactica is a large language model (LLM) designed for generating scientific text. It was available for three days from 15 November 2022, before being withdrawn for generating racist and inaccurate content. ==== Llama ==== Llama is an LLM released in February 2023. As of January 2026, the most recent release is the Llama 4. === Hardware === Meta used CPUs and in-house custom chips before 2022; they switched to Nvidia GPUs since then. MTIA v1, one of their early chips, is designed for the company's content recommendation algorithms. It was fabricated on TSMC's 7 nm process technology and consumed 25W, capable of 51.2 TFlops FP16. == Controversy == The French media outlet Mediapart reports that in 2022, Facebook's parent company illegally used works accumulated by the pirate site LibGen to train its artificial intelligence.
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Information flow

In discourse-based grammatical theory, information flow is any tracking of referential information by speakers. Information may be new, i.e., just introduced into the conversation; given, i.e., already active in the speakers' consciousness; or old, i.e., no longer active. The various types of activation, and how these are defined, are model-dependent. Information flow affects grammatical structures such as: Word order (topic, focus, and afterthought constructions). Active, passive, or middle voice. Choice of deixis, such as articles; "medial" deictics such as Spanish ese and Japanese sore are generally determined by the familiarity of a referent rather than by physical distance. Overtness of information, such as whether an argument of a verb is indicated by a lexical noun phrase, a pronoun, or not mentioned at all. Clefting: Splitting a single clause into two clauses, each with its own verb, e.g. ‘The chicken turtles tasted like chicken.’ becomes ‘It was the chicken turtle | that tasted like chicken.’ In this case, clefting is used to shift the focus of the sentence to the subject, the chicken turtle. Front focus: Placing at the start (front) of a sentence information that would normally occur later in the sentence, to give it extra prominence. For example, in pop culture, Yoda's speech often utilizes such syntactic construction, such as when he says 'much to learn you still have' to Luke Skywalker. End focus (or end weight): Given or familiar information followed by new information. This gives prominence to the final part of the sentences and can enable suspense to build, e.g. ‘Through the door came a gigantic wolf’.(Umer Prince)
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Single-source publishing

Single-source publishing, also known as single-sourcing publishing, is a content management method which allows the same source content to be used across different forms of media and more than one time. The labor-intensive and expensive work of editing need only be carried out once, on only one document; that source document (the single source of truth) can then be stored in one place and reused. This reduces the potential for error, as corrections are only made one time in the source document. The benefits of single-source publishing primarily relate to the editor rather than the user. The user benefits from the consistency that single-sourcing brings to terminology and information. This assumes the content manager has applied an organized conceptualization to the underlying content (A poor conceptualization can make single-source publishing less useful). Single-source publishing is sometimes used synonymously with multi-channel publishing though whether or not the two terms are synonymous is a matter of discussion. == Definition == While there is a general definition of single-source publishing, there is no single official delineation between single-source publishing and multi-channel publishing, nor are there any official governing bodies to provide such a delineation. Single-source publishing is most often understood as the creation of one source document in an authoring tool and converting that document into different file formats or human languages (or both) multiple times with minimal effort. Multi-channel publishing can either be seen as synonymous with single-source publishing, or similar in that there is one source document but the process itself results in more than a mere reproduction of that source. == History == The origins of single-source publishing lie, indirectly, with the release of Windows 3.0 in 1990. With the eclipsing of MS-DOS by graphical user interfaces, help files went from being unreadable text along the bottom of the screen to hypertext systems such as WinHelp. On-screen help interfaces allowed software companies to cease the printing of large, expensive help manuals with their products, reducing costs for both producer and consumer. This system raised opportunities as well, and many developers fundamentally changed the way they thought about publishing. Writers of software documentation did not simply move from being writers of traditional bound books to writers of electronic publishing, but rather they became authors of central documents which could be reused multiple times across multiple formats. The first single-source publishing project was started in 1993 by Cornelia Hofmann at Schneider Electric in Seligenstadt, using software based on Interleaf to automatically create paper documentation in multiple languages based on a single original source file. XML, developed during the mid- to late-1990s, was also significant to the development of single-source publishing as a method. XML, a markup language, allows developers to separate their documentation into two layers: a shell-like layer based on presentation and a core-like layer based on the actual written content. This method allows developers to write the content only one time while switching it in and out of multiple different formats and delivery methods. In the mid-1990s, several firms began creating and using single-source content for technical documentation (Boeing Helicopter, Sikorsky Aviation and Pratt & Whitney Canada) and user manuals (Ford owners manuals) based on tagged SGML and XML content generated using the Arbortext Epic editor with add-on functions developed by a contractor. The concept behind this usage was that complex, hierarchical content that did not lend itself to discrete componentization could be used across a variety of requirements by tagging the differences within a single document using the capabilities built into SGML and XML. Ford, for example, was able to tag its single owner's manual files so that 12 model years could be generated via a resolution script running on the single completed file. Pratt & Whitney, likewise, was able to tag up to 20 subsets of its jet engine manuals in single-source files, calling out the desired version at publication time. World Book Encyclopedia also used the concept to tag its articles for American and British versions of English. Starting from the early 2000s, single-source publishing was used with an increasing frequency in the field of technical translation. It is still regarded as the most efficient method of publishing the same material in different languages. Once a printed manual was translated, for example, the online help for the software program which the manual accompanies could be automatically generated using the method. Metadata could be created for an entire manual and individual pages or files could then be translated from that metadata with only one step, removing the need to recreate information or even database structures. Although single-source publishing is now decades old, its importance has increased urgently as of the 2010s. As consumption of information products rises and the number of target audiences expands, so does the work of developers and content creators. Within the industry of software and its documentation, there is a perception that the choice is to embrace single-source publishing or render one's operations obsolete. == Criticism == Editors using single-source publishing have been criticized for below-standard work quality, leading some critics to describe single-source publishing as the "conveyor belt assembly" of content creation. While heavily used in technical translation, there are risks of error in regard to indexing. While two words might be synonyms in English, they may not be synonyms in another language. In a document produced via single-sourcing, the index will be translated automatically and the two words will be rendered as synonyms. This is because they are synonyms in the source language, while in the target language they are not.
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Irish logarithm

The Irish logarithm was a system of number manipulation invented by Percy Ludgate for machine multiplication. The system used a combination of mechanical cams as lookup tables and mechanical addition to sum pseudo-logarithmic indices to produce partial products, which were then added to produce results. The technique is similar to Zech logarithms (also known as Jacobi logarithms), but uses a system of indices original to Ludgate. == Concept == Ludgate's algorithm compresses the multiplication of two single decimal numbers into two table lookups (to convert the digits into indices), the addition of the two indices to create a new index which is input to a second lookup table that generates the output product. Because both lookup tables are one-dimensional, and the addition of linear movements is simple to implement mechanically, this allows a less complex mechanism than would be needed to implement a two-dimensional 10×10 multiplication lookup table. Ludgate stated that he deliberately chose the values in his tables to be as small as he could make them; given this, Ludgate's tables can be simply constructed from first principles, either via pen-and-paper methods, or a systematic search using only a few tens of lines of program code. They do not correspond to either Zech logarithms, Remak indexes or Korn indexes. == Pseudocode == The following is an implementation of Ludgate's Irish logarithm algorithm in the Python programming language: Table 1 is taken from Ludgate's original paper; given the first table, the contents of Table 2 can be trivially derived from Table 1 and the definition of the algorithm. Note since that the last third of the second table is entirely zeros, this could be exploited to further simplify a mechanical implementation of the algorithm.
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IRows

iRows was a web-based spreadsheet in beta with a GUI similar to the traditional desktop-based spreadsheet applications, such as Microsoft Excel and OpenOffice.org. It was shut down on December 31, 2006, after it was announced that its two founders had been hired by Google. iRows used Ajax and XML. It was described as an example of a Web 2.0 system. iRows supported conventional spreadsheet features functions, value formatting and charts and added web oriented spreadsheet capabilities like collaboration (multiple people using a shared spreadsheet, sending a spreadsheet as a link instead of an attachment and ability to publish spreadsheets on other web pages (e.g. blogs).
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Knowledge organization

Knowledge organization (KO), organization of knowledge, organization of information, or information organization is an intellectual discipline concerned with activities such as document description, indexing, and classification that serve to provide systems of representation and order for knowledge and information objects. According to The Organization of Information by Joudrey and Taylor, information organization: examines the activities carried out and tools used by people who work in places that accumulate information resources (e.g., books, maps, documents, datasets, images) for the use of humankind, both immediately and for posterity. It discusses the processes that are in place to make resources findable, whether someone is searching for a single known item or is browsing through hundreds of resources just hoping to discover something useful. Information organization supports a myriad of information-seeking scenarios. Issues related to knowledge sharing can be said to have been an important part of knowledge management for a long time. Knowledge sharing has received a lot of attention in research and business practice both within and outside organizations and its different levels. Sharing knowledge is not only about giving it to others, but it also includes searching, locating, and absorbing knowledge. Unawareness of the employees' work and duties tends to provoke the repetition of mistakes, the waste of resources, and duplication of the same projects. Motivating co-workers to share their knowledge is called knowledge enabling. It leads to trust among individuals and encourages a more open and proactive relationship that grants the exchange of information easily. Knowledge sharing is part of the three-phase knowledge management process which is a continuous process model. The three parts are knowledge creation, knowledge implementation, and knowledge sharing. The process is continuous, which is why the parts cannot be fully separated. Knowledge creation is the consequence of individuals' minds, interactions, and activities. Developing new ideas and arrangements alludes to the process of knowledge creation. Using the knowledge which is present at the company in the most effective manner stands for the implementation of knowledge. Knowledge sharing, the most essential part of the process for our topic, takes place when two or more people benefit by learning from each other. Traditional human-based approaches performed by librarians, archivists, and subject specialists are increasingly challenged by computational (big data) algorithmic techniques. KO as a field of study is concerned with the nature and quality of such knowledge-organizing processes (KOP) (such as taxonomy and ontology) as well as the resulting knowledge organizing systems (KOS). == Theoretical approaches == === Traditional approaches === Among the major figures in the history of KO are Melvil Dewey (1851–1931) and Henry Bliss (1870–1955). Dewey's goal was an efficient way to manage library collections; not an optimal system to support users of libraries. His system was meant to be used in many libraries as a standardized way to manage collections. The first version of this system was created in 1876. An important characteristic in Henry Bliss' (and many contemporary thinkers of KO) was that the sciences tend to reflect the order of Nature and that library classification should reflect the order of knowledge as uncovered by science: The implication is that librarians, in order to classify books, should know about scientific developments. This should also be reflected in their education: Again from the standpoint of the higher education of librarians, the teaching of systems of classification ... would be perhaps better conducted by including courses in the systematic encyclopedia and methodology of all the sciences, that is to say, outlines which try to summarize the most recent results in the relation to one another in which they are now studied together. ... (Ernest Cushing Richardson, quoted from Bliss, 1935, p. 2) Among the other principles, which may be attributed to the traditional approach to KO are: Principle of controlled vocabulary Cutter's rule about specificity Hulme's principle of literary warrant (1911) Principle of organizing from the general to the specific Today, after more than 100 years of research and development in LIS, the "traditional" approach still has a strong position in KO and in many ways its principles still dominate. === Facet analytic approaches === The date of the foundation of this approach may be chosen as the publication of S. R. Ranganathan's colon classification in 1933. The approach has been further developed by, in particular, the British Classification Research Group. The best way to explain this approach is probably to explain its analytico-synthetic methodology. The meaning of the term "analysis" is: breaking down each subject into its basic concepts. The meaning of the term synthesis is: combining the relevant units and concepts to describe the subject matter of the information package in hand. Given subjects (as they appear in, for example, book titles) are first analyzed into a few common categories, which are termed "facets". Ranganathan proposed his PMEST formula: Personality, Matter, Energy, Space and Time: Personality is the distinguishing characteristic of a subject. Matter is the physical material of which a subject may be composed. Energy is any action that occurs with respect to the subject. Space is the geographic component of the location of a subject. Time is the period associated with a subject. === The information retrieval tradition (IR) === Important in the IR-tradition have been, among others, the Cranfield experiments, which were founded in the 1950s, and the TREC experiments (Text Retrieval Conferences) starting in 1992. It was the Cranfield experiments, which introduced the measures "recall" and "precision" as evaluation criteria for systems efficiency. The Cranfield experiments found that classification systems like UDC and facet-analytic systems were less efficient compared to free-text searches or low level indexing systems ("UNITERM"). The Cranfield I test found, according to Ellis (1996, 3–6) the following results: Although these results have been criticized and questioned, the IR-tradition became much more influential while library classification research lost influence. The dominant trend has been to regard only statistical averages. What has largely been neglected is to ask: Are there certain kinds of questions in relation to which other kinds of representation, for example, controlled vocabularies, may improve recall and precision? === User-oriented and cognitive views === The best way to define this approach is probably by method: Systems based upon user-oriented approaches must specify how the design of a system is made on the basis of empirical studies of users. User studies demonstrated very early that users prefer verbal search systems as opposed to systems based on classification notations. This is one example of a principle derived from empirical studies of users. Adherents of classification notations may, of course, still have an argument: That notations are well-defined and that users may miss important information by not considering them. Folksonomies is a recent kind of KO based on users' rather than on librarians' or subject specialists' indexing. === Bibliometric approaches === These approaches are primarily based on using bibliographical references to organize networks of papers, mainly by bibliographic coupling (introduced by Kessler 1963) or co-citation analysis ( independently suggested by Marshakova 1973 and Small 1973). In recent years it has become a popular activity to construe bibliometric maps as structures of research fields. Two considerations are important in considering bibliometric approaches to KO: The level of indexing depth is partly determined by the number of terms assigned to each document. In citation indexing this corresponds to the number of references in a given paper. On the average, scientific papers contain 10–15 references, which provide quite a high level of depth. The references, which function as access points, are provided by the highest subject-expertise: The experts writing in the leading journals. This expertise is much higher than that which library catalogs or bibliographical databases typically are able to draw on. === The domain analytic approach === Domain analysis is a sociological-epistemological standpoint that advocates that the indexing of a given document should reflect the needs of a given group of users or a given ideal purpose. In other words, any description or representation of a given document is more or less suited to the fulfillment of certain tasks. A description is never objective or neutral, and the goal is not to standardize descriptions or make one description once and for all for different target groups. The develo
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Savepoint

A savepoint is a way of implementing subtransactions (also known as nested transactions) within a relational database management system by indicating a point within a transaction that can be "rolled back to" without affecting any work done in the transaction before the savepoint was created. Multiple savepoints can exist within a single transaction. Savepoints are useful for implementing complex error recovery in database applications. If an error occurs in the midst of a multiple-statement transaction, the application may be able to recover from the error (by rolling back to a savepoint) without needing to abort the entire transaction. A savepoint can be declared by issuing a SAVEPOINT name statement. All changes made after a savepoint has been declared can be undone by issuing a ROLLBACK TO SAVEPOINT name command. Issuing RELEASE SAVEPOINT name will cause the named savepoint to be discarded, but will not otherwise affect anything. Issuing the commands ROLLBACK or COMMIT will also discard any savepoints created since the start of the main transaction. Savepoints are defined in the SQL standard and are supported by all established SQL relational databases, including PostgreSQL, Oracle Database, Microsoft SQL Server, MySQL, IBM Db2, SQLite (since 3.6.8), Firebird, H2 Database Engine, and Informix (since version 11.50xC3).
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Maritime Informatics

Maritime Informatics is a thematic topic within the broader discipline of informatics. It can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from data science and artificial intelligence; as a field of study, it is positioned between computer science and marine engineering. == Beginnings of maritime informatics == As a result of the increasing levels of digitalisation occurring in the maritime sector starting around 2010 and stimulated by the EU-endorsed MonaLisa project for sea traffic management (STM), a number of academics and shipping industry leaders recognised that the maritime transportation sector would benefit from a specific field of study and application to be known as Maritime Informatics - the use of information systems, data sharing and data analytics in the business and operations of maritime transportation. They considered that it would lead to improvements in efficiency, safety, resilience, and ecological sustainability - all of which are currently lacking for many aspects of sea transport. One of the first public airings of the concept of Maritime Informatics was a presentation delivered on 11 September 2014 in Gothenburg, Sweden. A proposal for an inaugural minitrack on Maritime Informatics was accepted for the 2015 Americas Conference on Information Systems in Puerto Rico where three papers were presented. Since then numerous publications has been brought forward captured at www.maritimeinformatics.org and in late 2020 the first reference book on Maritime Informatics was co-written by 81 expert contributors (47 practitioners and 34 researchers) from 20 countries. Most impactful authors and journals in the domain have been documented in a review paper. Dimitrios Zissis, Luca Cazzanti and Leonardo M. Millefiori are the top three authors; top journals and conferences include Ocean Engineering, Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems, Sensors, the international Conference On Engineering, Technology And Innovation, Expert Systems With Applications, IEEE Access, and Journal of Navigation. == Background == The shipping industry has several particular organisational aspects that are recognised and taken into account in maritime informatics: It is predominantly a self-organising ecosystem Many activities are undertaken as part of episodic tight coupling There is a so-called maritime stack There is increasing pressure to balance capital productivity and energy efficiency There is the potential virtuous interplay between different types of systems == Data sharing == Digital data sharing is key to the all-important, arguably fundamental, data analytics aspects of maritime informatics because it opens the way for better access to relevant and reliable data. As in land-based commerce, digital data sharing is a growing phenomenon in maritime operations - though there is a way to go. It is enabling greater transparency for all those involved in the transportation of goods and passengers, not least being the end-customer. This leads to better and more informed decision-making and planning by all those involved. The push for digitalisation and data sharing is being pursued both by governments and the commercial sector. For example, the Member States of the IMO agreed a mandatory requirement for their governments to introduce electronic information exchange between ships and ports as from 8 April 2019. Meanwhile, commercial operators, particularly in the container lines are putting systems in place for sharing data for mutual benefit in their operations. Data sharing is an important aspect of the Port Collaborative Decision Making (PortCDM) and Port Call Optimization initiatives, both of which seek to improve the coordination, synchronization and efficiency of the port call process by enabling a common and shared situational awareness among all those involved. == Standardisation == The availability and sharing of relevant digital data underpins maritime informatics and is key to more effective and efficient coordination and synchronisation in the predominantly self-organising ecosystem that is maritime transportation. For this to occur, a high priority underpinning maritime informatics is the encouragement of standardised digital data exchange and data sharing, leading, in turn, to improvements in shipping analytics. Improved availability of data will support better historical analysis, now-casting and forecasting. The International Maritime Organization (IMO) FAL Committee is taking the lead in ensuring that the common terms used in the various standards being developed or in use in the maritime sector are compatible and therefore interoperable as far as is practicable, by creating and maintaining The IMO Compendium on Facilitation and Electronic Business. The IMO Compendium consists of an IMO Data Set and IMO Reference Data Model agreed by the main organisations involved in the development of standards for the electronic exchange of information related to the FAL Convention: the World Customs Organization (WCO), the United Nations Economic Commission for Europe (UNECE) and the International Organization for Standardization (ISO). There are several other prominent international governmental and non-governmental organisations actively contributing to the ongoing standardisation and harmonisation process including the UN Electronic Data Interchange for Administration, Commerce and Transport (UN EDIFACT), the Digital Container Shipping Association (DCSA), the International Harbour Masters Association (IHMA) and BIMCO - the world's largest direct-membership organisation for shipowners, charterers, shipbrokers and agents.
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Computer Graphics: Principles and Practice

Computer Graphics: Principles and Practice is a textbook written by James D. Foley, Andries van Dam, Steven K. Feiner, John Hughes, Morgan McGuire, David F. Sklar, and Kurt Akeley and published by Addison–Wesley. First published in 1982 as Fundamentals of Interactive Computer Graphics, it is widely considered a classic standard reference book on the topic of computer graphics. It is sometimes known as the bible of computer graphics (due to its size). == Editions == === First Edition === The first edition, published in 1982 and titled Fundamentals of Interactive Computer Graphics, discussed the SGP library, which was based on ACM's SIGGRAPH CORE 1979 graphics standard, and focused on 2D vector graphics. === Second Edition === The second edition, published 1990, was completely rewritten and covered 2D and 3D raster and vector graphics, user interfaces, geometric modeling, anti-aliasing, advanced rendering algorithms and an introduction to animation. The SGP library was replaced by SRGP (Simple Raster Graphics Package), a library for 2D raster primitives and interaction handling, and SPHIGS (Simple PHIGS), a library for 3D primitives, which were specifically written for the book. === Second Edition in C === In the second edition in C, published in 1995, all examples were converted from Pascal to C. New implementations for the SRGP and SPHIGS graphics packages in C were also provided. === Third Edition === A third edition covering modern GPU architecture was released in July 2013. Examples in the third edition are written in C++, C#, WPF, GLSL, OpenGL, G3D, or pseudocode. == Awards == The book has won a Front Line Award (Hall of Fame) in 1998.
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Algorithm IMED

In multi-armed bandit problems, IMED (for Indexed Minimum Empirical Divergence) is an algorithm developed in 2015 by Junya Honda and Akimichi Takemura. It is the first algorithm proved to be asymptotically optimal respect to the problem-dependant Lai–Robbins lower bound for distributions in ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} . == Multi-armed bandit problem == The Multi-armed bandit problem is a sequential game where one player has to choose at each turn between K {\displaystyle K} actions (arms). Behind every arm a {\displaystyle a} there is an unknown distribution ν a {\displaystyle \nu _{a}} that lies in a set D {\displaystyle {\mathcal {D}}} known by the player (for example, D {\displaystyle {\mathcal {D}}} can be the set of Gaussian distributions or Bernoulli distributions). At each turn t {\displaystyle t} the player chooses (pulls) an arm a t {\displaystyle a_{t}} , he then gets an observation X t {\displaystyle X_{t}} of the distribution ν a t {\displaystyle \nu _{a_{t}}} . === Regret minimization === The goal is to minimize the regret at time T {\displaystyle T} that is defined as R T := ∑ a = 1 K Δ a E [ N a ( T ) ] {\displaystyle R_{T}:=\sum _{a=1}^{K}\Delta _{a}\mathbb {E} [N_{a}(T)]} where μ a := E [ ν a ] {\displaystyle \mu _{a}:=\mathbb {E} [\nu _{a}]} is the mean of arm a {\displaystyle a} μ ∗ := max a μ a {\displaystyle \mu ^{}:=\max _{a}\mu _{a}} is the highest mean Δ a := μ ∗ − μ a {\displaystyle \Delta _{a}:=\mu ^{}-\mu _{a}} N a ( t ) {\displaystyle N_{a}(t)} is the number of pulls of arm a {\displaystyle a} up to turn t {\displaystyle t} The player has to find an algorithm that chooses at each turn t {\displaystyle t} which arm to pull based on the previous actions and observations ( a s , X s ) s < t {\displaystyle (a_{s},X_{s})_{s μ } {\displaystyle {\mathcal {K}}_{inf}(\nu ,\mu ,{\mathcal {D}}):=\inf \left\{\mathrm {KL} (\nu ,{\tilde {\nu }})\ |\ {\tilde {\nu }}\in {\mathcal {P}}([-\infty ,1]),\ \mathbb {E} [{\tilde {\nu }}]>\mu \right\}} K L {\displaystyle \mathrm {KL} } is the Kullback–Leibler divergence P ( [ − ∞ , 1 ] ) {\displaystyle {\mathcal {P}}([-\infty ,1])} is the set of distribution in [ − ∞ , 1 ] {\displaystyle [-\infty ,1]} ν ^ a ( t ) {\displaystyle {\hat {\nu }}_{a}(t)} is the empirical distribution of arm a {\displaystyle a} at turn t {\displaystyle t} μ ^ ∗ ( t ) {\displaystyle {\hat {\mu }}^{}(t)} is the highest empirical mean of turn t {\displaystyle t} Remark : For arms a {\displaystyle a} that verify μ ^ a ( t ) = μ ^ ∗ ( t ) {\displaystyle {\hat {\mu }}_{a}(t)={\hat {\mu }}^{}(t)} we have K i n f ( ν ^ a ( t ) , μ ^ ∗ ( t ) ) = 0 {\displaystyle K_{inf}({\hat {\nu }}_{a}(t),{\hat {\mu }}^{}(t))=0} . Then there index is equal to ln ⁡ ( N a ( t ) ) {\displaystyle \ln(N_{a}(t))} === Pseudocode === for each arm i do: n[i] ← 1; nu[i] ← None; mu[i] ← None for t from 1 to K do: select arm t observe reward r n[t] ← n[t] + 1 nu[t] ← update empirical distribution mu[t] ← update empirical mean for t from K+1 to T do: mu ← highest mu for each arm i do: scoreK[i] ← n[i] K_inf(nu[i],mu) scoreN[i] ← ln(n[i]) index[i] ← scoreK[i] + scoreN[i] select arm a with smallest index[a] observe reward r n[a] ← n[a] + 1 nu[a] ← update empirical distribution mu[a] ← update empirical mean == Theoretical results == In the multi-armed bandit problem we have the asymptotic Lai–Robbins lower bound asymptotic lower bound on regret. The algorithm IMED is the first algorithm that matches this lower bound for distribution in ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} in the first order. If the distribution are also bounded then it also match the second order. It is the first algorithm that match the second under of this lower bound. === Lai–Robbins lower bound === In 1985 Lai and Robbins proved an asymptotic, problem-dependent lower bound on regret. In 2018, Aurelien Garivier, Pierre Menard and Gilles Stoltz proved a refined lower bound that gives the second order It states that for every consistent algorithm on the set P ( [ − ∞ , 1 ] ) {\displaystyle {\mathcal {P}}([-\infty ,1])} — that is, an algorithm for which, for every ( ν 1 , … , ν K ) ∈ P ( [ − ∞ , 1 ] ) K {\displaystyle (\nu _{1},\dots ,\nu _{K})\in {\mathcal {P}}([-\infty ,1])^{K}} , the regret R T {\displaystyle R_{T}} is subpolynomial (i.e. R T = o T → + ∞ ( T α ) {\displaystyle R_{T}=o_{T\to +\infty }(T^{\alpha })} for all α > 0 {\displaystyle \alpha >0} ) — we have: R T ≥ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T − Ω T → + ∞ ( ln ⁡ ln ⁡ T ) . {\displaystyle R_{T}\geq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T-\Omega _{T\to +\infty }(\ln \ln T).} This bound is asymptotic (as T → + ∞ {\displaystyle T\to +\infty } ) and gives a first-order lower bound of order ln ⁡ T {\displaystyle \ln T} with the optimal constant in front of it and the second order in − Ω ( ln ⁡ ln ⁡ T ) {\displaystyle -\Omega (\ln \ln T)} . === Regret bound for IMED === If the distribution of every arm a {\displaystyle a} is ( − ∞ , 1 ] {\displaystyle (-\infty ,1]} ( i.e. ν a ∈ P ( [ − ∞ , 1 ] ) ) {\displaystyle \nu _{a}\in {\mathcal {P}}([-\infty ,1]))} then the regret of the algorithm IMED verify R T ≤ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T + O ( 1 ) {\displaystyle R_{T}\leq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T+O(1)} If all the distribution ν a {\displaystyle \nu _{a}} are bounded then it exists a constant C > 0 {\displaystyle C>0} such that for T {\displaystyle T} large enough the regret of IMED is upper bounded by R T ≤ ( ∑ a : μ a < μ ∗ Δ a K inf ( ν a , μ ∗ ) ) ln ⁡ T − C ln ⁡ ln ⁡ T {\displaystyle R_{T}\leq \left(\sum _{a:\mu _{a}<\mu ^{}}{\frac {\Delta _{a}}{{\mathcal {K}}_{\inf }(\nu _{a},\mu ^{})}}\right)\ln T-C\ln \ln T} == Computation time == The algorithm only requiere to compute the K i n f {\displaystyle K_{inf}} for suboptimal arms who are pulled O ( ln ⁡ T ) {\displaystyle O(\ln T)} times, which make it a lot faster than KL-UCB. A faster version of IMED was developed in 2023 to make it even faster, using a Taylor development of the K i n f {\displaystyle K_{inf}} in the first order .
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Microsoft Office PerformancePoint Server

Microsoft Office PerformancePoint Server is a business intelligence software product released in 2007 by Microsoft. The product was generally an integration of the acquisitions from ProClarity - the Planning Server and Monitoring Server - into Microsoft's SharePoint server product line. Although discontinued in 2009, the dashboard, scorecard, and analytics capabilities of PerformancePoint Server were incorporated into SharePoint 2010 and later versions. PerformancePoint Server also provided a planning and budgeting component directly integrated with Excel. == History == Microsoft offered preview releases of PerformancePoint Server starting in mid-2006. Previews of the product were formed from Business Scorecard Manager 2005 and the Planning Server component. Acquisitions ProClarity and Great Plains brought additional analytics and planning/reporting capabilities, as well as companion products ProClarity 6.3 and FRx. PerformancePoint Server was officially released in November 2007. Microsoft discontinued PerformancePoint Server as an independent product in 2009 and folded its dashboard, scorecard and analytics capabilities into PerformancePoint Services in SharePoint Server 2010. == Monitoring Server Component == Business monitoring capabilities, including dashboards, scorecards & key performance indicators, navigable reports for deeper analysis, strategy maps, and linked filtering, are provided by PerformancePoint's Monitoring Server component. A Dashboard Designer application that is distributed from Monitoring Server enables business analysts or IT Administrators to: create & test data source connections create views that use those data connections assemble the views into a dashboard deploy the dashboard as a SharePoint page Dashboard Designer saved content and security information back to the Monitoring Server. Data source connections, such as OLAP cubes or relational tables, were also made through Monitoring Server. After a dashboard has been published to the Monitoring Server database, it would be deployed as a SharePoint page and shared with other users as such. When the pages were opened in a web browser, Monitoring Server updated the data in the views by connecting back to the original data sources. == Planning Server Component == PerformancePoint's Planning Server component supported maintenance of logical business models, budget & approval workflows, enterprise data sources, and it followed Generally Accepted Accounting Principles. Planning Server made use of Excel for input and line-of-business reporting, as well as SQL Server for storing and processing business models. == Management Reporter Component == The Management Reporter component was designed to perform financial reporting and can read PerformancePoint Planning models directly. A development kit was also available to allow this component to read other models.
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