AI Chatbot Creator

AI Chatbot Creator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Verbot

The Verbot (short for Verbal-Robot) was a chatbot program and artificial intelligence software development kit (SDK) designed for Windows and web platforms. == Early beginning == The origin of verbot traces back to Michael Mauldin's research during his time as a graduate student and post-doctoral fellow at Carnegie Mellon University. The creative foundation also stems from Peter Plantec's work in personality psychology and art direction. === Historic outline === In 1994, Michael Loren Mauldin, founder of Lycos, Inc., developed a prototype chatbot, Julia, which competed in the internationally known Turing test, for the coveted Loebner Prize. The Turing test matches computer scientist judges against machines to see if they can distinguish a computer from a real human. Julia was refined and developed, and in 1997, Dr. Mauldin and Peter Plantec, a clinical psychologist and animator, formed Virtual Personalities, Inc. (now Conversive, Inc.) in order to create a virtual human interface that would incorporate real-time animation as well as speech and natural language processing. The initial release, a stand-alone virtual person called Sylvie, was beta-tested to the public. This release was well received, and finally, after several versions, the production release (deemed version 3) of the Verbally Enhanced Software Robot, or Verbot, was deployed in fall 2000. The grandfather of all Verbots is Rog-O-Matic, which, although it could not talk, could and did explore a virtual world. Julia has been active on the internet in one form or another since 1989. A close cousin of Julia is Lycos, a robot that explores the World Wide Web and answers questions about it. Sylvie was the first Verbot with a face and a voice. Sylvie was the first Virtual Human with advanced, flexible interfacing capability. === Beginnings === The Virtual Personalities story goes back to 1978, where Mauldin was attending Rice University. Fascinated by the idea of ELIZA, he proceeded to write a program called "PET" for his 8 kilobyte Commodore PET Computer. PET included simple induction as a way to post new information, for example: Subject: I like my friend (later) Subject: I like food. PET: I have heard that food is your friend. Meanwhile, Plantec was separately designing a personality for "Entity", a theoretical virtual human that would interact comfortably with humans without pretending to be one. At that time the technology was not advanced enough to realize Entity. Mauldin got so involved with this that he majored in Computer Science and minored in Linguistics. === Rogue === In the late seventies and early eighties, a popular computer game at universities was Rogue, an implementation of Dungeons and Dragons where the player would descend 26 levels in a randomly created dungeon, fighting monsters, gathering treasure, and searching for the elusive "Amulet of Yendor". Mauldin was one of four grad students who devoted a large amount of time to building a program called "Rog-O-Matic" capable of retrieving the amulet and emerging victorious from the dungeon. === TinyMUD === In 1989, when James Aspnes at Carnegie Mellon created the first TinyMUD (a descendant of MUD and AberMUD), Mauldin was one of the first to create a computer player that would explore the text-based world of TinyMUD. But his first robot, Gloria, gradually accreted more and more linguistic ability, to the point that it could pass the "unsuspecting" Turing test. In this version of the test, the human has no reason to suspect that one of the other occupants of the room is controlled by a computer, and so is more polite and asks fewer probing questions. The second generation of Mauldin's TinyMUD robots was Julia, created on Jan. 8, 1990. Julia slowly developed into a more and more capable conversational agent, and assumed useful duties in the TinyMUD world, including tour guide, information assistant, note-taker, and message-relayer. She could even play the card game hearts along with the other human players. In 1991, Julia attended the first Loebner Prize contest in Boston, Massachusetts. Although she only finished third, she was ranked by one judge as more human than one of the human confederates, winning a coveted certificate of humanness in the world's first restricted Turing test. Julia continued to log in to various TinyMUD's and TinyMucks for the next seven years, and chatted with hundreds of people a month over the internet. === Lycos === Julia's job was to explore a virtual world consisting of pages of textual descriptions, with links between them, and to construct an internal map of that world and answer questions about it (including path information such as the shortest route from one room to another, and matching information, such as which rooms contained a certain kind of object or textual description). It was therefore only a very short cognitive leap from Julia to Lycos, another robotic agent that explores a virtual world made of hyperlinked pages of text, and which answers questions about those pages. Sylvie was born and her abilities were expanded greatly to include interfacing with computers and control systems via her serial ports. === Sylvie === Sylvie was the first intelligent animated virtual human. She was designed both as a conversation agent and as a virtual human interface that would form a bridge between the two. She became more popular as a conversation agent, but her designers believe she serves as a prototype for future virtual human interface design that will help us all cope with the increasing complexity of technology. As an aside, Plantec noticed that a large number of Sylvies have been sold in Southeast Asia. Upon investigation, he found out that students had discovered a "test" mode that would allow them to type in English sentences that Sylvie would pronounce in her somewhat stylized English. == Ownership == In 1997, Dr. Mauldin and Peter Plantec formed Virtual Personalities, Inc. to create Natural Language Processing solutions for companies. In 2001 Virtual Personalities, Inc. became Conversive, Inc. to reflect the focus on providing Customer Service and Marketing to the Enterprise Market. In late 2012 Avaya, Inc. acquired Conversive's assets including Verbots. == Verbot versions == The Verbot 4 version was created and released in 2004. In 2005 Version 4.1 of the Verbot Software was released with many feature enhancements and bug fixes, including built-in support for embedding C# code in outputs and conditionals. In early 2006 Conversive launched Verbots Online allowing Verbot 4 users to upload their knowledge and show off their bots to the world. In 2009 Version 5 was released, completely free and fully featured. In early 2012 the last version of Verbot, 5.0.1.2, was released to the general public with support for Windows 7. Later in 2012 Verbots Online completely shut down. == Verbots today == Verbots.com, its community of users, and its forums no longer exist, but the software and users can still be found. There has been no active development since the early 2012 release of Verbot 5.0.1.2.
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Corpus language

A corpus language is a language that has no living speakers but for which numerous records produced by its native speakers survive. Examples of corpus languages are Ancient Greek, Latin, the Egyptian language, Old English, Old Norse, Elamite, and Sanskrit. Some corpus languages, such as Ancient Greek and Latin, left very large corpora and therefore can be fully reconstructed, even though some details of pronunciation may be unclear. Such languages can be used even today, as is the case with Sanskrit and Latin. Other languages have such limited corpora that some important words—e.g., some pronouns—are lacking in the corpora. Examples of these are Ugaritic and Gothic. Languages attested only by a few words, often names, and a few phrases, are called Trümmersprache (literally "rubble languages") in German linguistics. These can be reconstructed only in a very limited way, and often their genetic relationship to other languages remains unclear. Examples are Dalmatian, Etruscan, also known as Rasenna, Dadanitic, a Semitic language that may be close to classical Arabic, Lombardic, Burgundian, Vandalic, and Oscan, Umbrian, and Faliscan, all Italic languages that were related to Latin. Corpus languages are studied using the methods of corpus linguistics, but corpus linguistics can also be used (and is commonly used) for the study of the writings and other records of living languages. Not all extinct languages are corpus languages, since there are many extinct languages in which few or no writings or other records survive, as is the case in the vast majority of languages that have ever existed.
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METAL MT

A machine translation system developed at the University of Texas and at Siemens which ran on Lisp Machines. == Background == Originally titled the Linguistics Research System (LRS), it was later renamed METAL (Mechanical Translation and Analysis of Languages). It started life as a German-English system funded by the USAF. == 1980 == A copy of the Weidner Multi-Lingual Word Processing software was requested by the German Government for the Siemens Corporation of Germany in September 1980 and was nicknamed the Siemens-Weidner Engine (originally English-German). This revolutionary multilingual word processing engine became foundational in the development of the Metal MT project, according to John White of the Siemens Corporation. After the Metal MT, development Rights to the Siemens-Weidner Engine were sold to a Belgium company, Lernout & Hauspie. The Siemens copy of the Weidner Multilingual Word Processing software has since been acquired through the purchase of assets of Lernout & Hauspie by Bowne Global Solutions, Inc., which was later acquired by Lionbridge Technologies, Inc. and is demonstrated in their itranslator software.
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Cortana (virtual assistant)

Cortana is a discontinued virtual assistant developed by Microsoft that used the Bing search engine to perform tasks such as setting reminders and answering questions for users. Cortana was available in English, Portuguese, French, German, Italian, Spanish, Chinese, and Japanese language editions, depending on the software platform and region in which it was used. In 2019, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and on March 31, 2021, the Cortana mobile app was shut down globally. On June 2, 2023, Microsoft announced that support for the Cortana standalone app on Microsoft Windows would end in late 2023 and would be replaced by Microsoft Copilot, an AI chatbot. Support for Cortana in the Microsoft Outlook and Microsoft 365 mobile apps was discontinued in fall of 2023. == History == === Beginnings (2009–2014) === The development of Cortana started in 2009 in the Microsoft Speech products team with general manager Zig Serafin and Chief Scientist Larry Heck. Heck and Serafin established the vision, mission, and long-range plan for Microsoft's digital personal assistant and they built a team with the expertise to create the initial prototypes for Cortana. Some of the key researchers in these early efforts included Microsoft Research researchers Dilek Hakkani-Tür, Gokhan Tur, Andreas Stolcke, and Malcolm Slaney, research software developer Madhu Chinthakunta, and user experience designer Lisa Stifelman. To develop the Cortana digital assistant, the team interviewed human personal assistants. The interviews inspired a number of unique features in Cortana, including the assistant's "notebook" feature. Originally, Cortana was meant to be only a codename, but a petition on Windows Phone's UserVoice site proved to be popular and made the codename official. Cortana was demonstrated for the first time at the Microsoft Build developer conference in San Francisco in April 2014. It was launched as a key ingredient of Microsoft's planned "makeover" of future operating systems for Windows Phone and Windows. It was named after Cortana, a synthetic intelligence character in Microsoft's Halo video game franchise originating in Bungie folklore, with Jen Taylor, the character's voice actress, returning to voice the personal assistant's US-specific version. === Expansion (2015–2018) === In January 2015, Microsoft announced the availability of Cortana for Windows 10 desktops and mobile devices as part of merging Windows Phone into the operating system at large. On May 26, 2015, Microsoft announced that Cortana would also be available on other mobile platforms. An Android release was set for July 2015, but the Android APK file containing Cortana was leaked ahead of its release. It was officially released, along with an iOS version, in December 2015. During E3 2015, Microsoft announced that Cortana would come to the Xbox One as part of a universally designed Windows 10 update for the console. Microsoft integrated Cortana into numerous products such as Microsoft Edge. Microsoft's Cortana assistant was deeply integrated into the browser. Cortana was able to find opening hours when on restaurant sites, show retail coupons for websites, or show weather information in the address bar. At the Worldwide Partners Conference 2015 Microsoft demonstrated Cortana integration with products such as GigJam. Conversely, Microsoft announced in late April 2016 that it would block anything other than Bing and Edge from being used to complete Cortana searches, again raising questions of anti-competitive practices by the company. Microsoft's "Windows in the car" concept included Cortana. The concept makes it possible for drivers to make restaurant reservations and see places before they go there. At Microsoft Build 2016, Microsoft announced plans to integrate Cortana into Skype (Microsoft's video-conferencing and instant messaging service) as a bot to allow users to order food, book trips, transcribe video messages and make calendar appointments through Cortana in addition to other bots. As of 2016, Cortana was able to underline certain words and phrases in Skype conversations that relate to contacts and corporations. A writer from Engadget has criticised the Cortana integration in Skype for responding only to very specific keywords, feeling as if she was "chatting with a search engine" due to the impersonal way the bots replied to certain words such as "Hello" causing the Bing Music bot to bring up Adele's song of that name. Microsoft also announced at Microsoft Build 2016 that Cortana would be able to cloud-synchronise notifications between Windows 10 Mobile's and Windows 10's Action Center, as well as notifications from Android devices. In December 2016, Microsoft announced the preview of Calendar.help, a service that enabled people to delegate the scheduling of meetings to Cortana. Users interact with Cortana by including her in email conversations. Cortana would then check people's availability in Outlook Calendar or Google Calendar, and work with others Cc'd on the email to schedule the meeting. The service relied on automation and human-based computation. In May 2017, Microsoft announced INVOKE, a voice-activated speaker featuring Cortana, in collaboration with Harman Kardon. The premium speaker has a cylindrical design and offers 360-degree sound, the ability to make and receive calls with Skype, and all of the other features currently available with Cortana. In 2017, Microsoft partnered with Amazon to integrate Echo and Cortana with each other, allowing users of each smart assistant to summon the other via a command. This feature preview was released in August 2018. Windows 10 users were able to just say "Hey Cortana, open Alexa" and Echo users were able to say "Alexa, open Cortana" to summon the other assistant. === Decreasing focus and discontinuation (2019–2024) === In January 2019, Microsoft CEO Satya Nadella stated that he no longer saw Cortana as a direct competitor against Alexa and Siri. Shortly thereafter, Microsoft began reducing the prevalence of Cortana and converting it from an assistant into different software integrations. It was split from the Windows 10 search bar in April 2019. In January 2020, the Cortana mobile app was removed from certain markets, and then, on July 24, 2020, Cortana was removed from the Xbox dashboard as part of a redesign. On January 31, 2021, Microsoft removed the Cortana mobile application in many markets, including the UK, Australia, Germany, Mexico, China, Spain, Canada, and India. On March 31, 2021, Microsoft shut down the Cortana apps globally for iOS and Android and removed the apps entirely from their corresponding app stores. To access previously recorded content, users had to use Cortana on Windows 10 or other specialized Microsoft applications. Microsoft also reduced emphasis on Cortana in Windows with the 2021 release of Windows 11. Cortana was not used during the device setup process or pinned to the taskbar by default. On June 2, 2023, Microsoft announced the Cortana standalone app on Windows 10 and Windows 11 which would shut down later in the year. In its support article, Microsoft listed several alternatives, most of which have since been rebranded as Microsoft Copilot. They also added that the change would not impact Cortana in Office 365 and Teams environments. On August 11, 2023, Microsoft updated the Cortana standalone app in Windows, informing that it was deprecated and can no longer be used. Microsoft's support article announcing the deprecation of Cortana was updated to reflect this change. Along with the deprecation of the standalone app, it was announced that Cortana support in Teams mobile, Microsoft Teams displays, and Teams rooms would end in late 2023. The support article states that Cortana in the “Play my emails” feature of the Microsoft Outlook mobile app would continue to be available. Later in June 2024, the support article was updated, stating that Cortana in the voice search and the "Play my emails" feature is now removed from the Microsoft Outlook mobile app, officially marking the discontinuation of Cortana across all Microsoft products. On May 22, 2024, Microsoft announced the Windows 11 24H2 update, which removed Cortana, Tips, and WordPad from systems. == Functionality == Cortana was able to set reminders, recognize natural voice without the requirement for keyboard input, and answer questions using information from the Bing search engine. Searches using Windows 10 are made only with the Microsoft Bing search engine, and all links will open with Microsoft Edge, except when a screen reader such as Narrator was being used, where the links will open in Internet Explorer. Windows Phone 8.1's universal Bing SmartSearch features were incorporated into Cortana, which replaced the
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Metadata repository

A metadata repository is a database created to store metadata. Metadata is information about the structures that contain the actual data. Metadata is often said to be "data about data", but this is misleading. Data profiles are an example of actual "data about data". Metadata adds one layer of abstraction to this definition– it is data about the structures that contain data. Metadata may describe the structure of any data, of any subject, stored in any format. A well-designed metadata repository typically contains data far beyond simple definitions of the various data structures. Typical repositories store dozens to hundreds of separate pieces of information about each data structure. Comparing the metadata of a couple data items - one digital and one physical - clarify what metadata is: First, digital: For data stored in a database one may have a table called "Patient" with many columns, each containing data which describes a different attribute of each patient. One of these columns may be named "Patient_Last_Name". What is some of the metadata about the column that contains the actual surnames of patients in the database? We have already used two items: the name of the column that contains the data (Patient_Last_Name) and the name of the table that contains the column (Patient). Other metadata might include the maximum length of last name that may be entered, whether or not last name is required (can we have a patient without Patient_Last_Name?), and whether the database converts any surnames entered in lower case to upper case. Metadata of a security nature may show the restrictions which limit who may view these names. Second, physical: For data stored in a brick and mortar library, one have many volumes and may have various media, including books. Metadata about books would include ISBN, Binding_Type, Page_Count, Author, etc. Within Binding_Type, metadata would include possible bindings, material, etc. This contextual information of business data include meaning and content, policies that govern, technical attributes, specifications that transform, and programs that manipulate. == Definition == The metadata repository is responsible for physically storing and cataloging metadata. Data in a metadata repository should be generic, integrated, current, and historical: Generic Meta model should store the metadata by generic terms instead of storing it by an applications-specific defined way, so that if your data base standard changes from one product to another the physical meta model of the metadata repository would not need to change. Integration of the metadata repository allows all business areas' metadata to be in an integrated fashion: Covering all domains and subject areas of the organization. current and historical The metadata repository should have accessible current and historical metadata. Metadata repositories used to be referred to as a data dictionary. With the transition of needs for the metadata usage for business intelligence has increased so is the scope of the metadata repository increased. Earlier data dictionaries are the closest place to interact technology with business. Data dictionaries are the universe of metadata repository in the initial stages but as the scope increased Business glossary and their tags to variety of status flags emerged in the business side while consumption of the technology metadata, their lineage and linkages made the repository, the source for valuable reports to bring business and technology together and helped data management decisions easier as well as assess the cost of the changes. Metadata repository explores the enterprise wide data governance, data quality and master data management (includes master data and reference data) and integrates this wealth of information with integrated metadata across the organization to provide decision support system for data structures, even though it only reflects the structures consumed from various systems. == Repository vs. registry == Repository has additional functionalities compared with registry. Metadata repository not only stores metadata like Metadata registry but also adds relationships with related metadata types. Metadata when related in a flow from its point of entry into organization up to the deliverables is considered as the lineage of that data point. Metadata when related across other related metadata types is called linkages. By providing the relationships to all the metadata points across the organization and maintaining its integrity with an architecture to handle the changes, metadata repository provides the basic material for understanding the complete data flow and their definitions and their impact. Also the important feature is to maintain the version control though this statement for contrasting is open for discussion. These definitions are still evolving, so the accuracy of the definitions needs refinement. The purpose of registry is to define the metadata element and maintained across the organization. And data models and other data management teams refer to the registry for any changes to follow. While Metadata repository sources metadata from various metadata systems in the organizations and reflects what is in the upstream. Repository never acts as an upstream while registry is used as an upstream for metadata changes. == Reason for use == Metadata repository enables all the structure of the organizations data containers to one integrated place. This opens plethora of resourceful information for making calculated business decisions. This tool uses one generic form of data model to integrate all the models thus brings all the applications and programs of the organization into one format. And on top of it applying the business definitions and business processes brings the business and technology closer that will help organizations make reliable roadmaps with definite goals. With one stop information, business will have more control on the changes, and can do impact analysis of the tool. Usually business spends much time and money to make decisions based on discovery and research on impacts to make changes or to add new data structures or remove structures in data management of the organization. With a structured and well maintained repository, moving the product from ideation to delivery takes the least amount of time (considering other variables are constant). To sum it up: Integration of the metadata across the organization Build relationship between various metadata types Build relationship between various disparate systems Define business golden copy of definitions Version control of the changes at structure level Interaction with Reference data Link view to master data Automatic synchronization with various authorized metadata source systems More control to business decisions Validate the structures by overlapping the models Discovering discrepancies, gaps, lineage, metrics at data structure level Each database management system (DBMS) and database tools have their own language for the metadata components within. Database applications already have their own repositories or registries that are expected to provide all of the necessary functionality to access the data stored within. Vendors do not want other companies to be capable of easily migrating data away from their products and into competitors products, so they are proprietary with the way they handle metadata. CASE tools, DBMS dictionaries, ETL tools, data cleansing tools, OLAP tools, and data mining tools all handle and store metadata differently. Only a metadata repository can be designed to store the metadata components from all of these tools. == Design == Metadata repositories should store metadata in four classifications: ownership, descriptive characteristics, rules and policies, and physical characteristics. Ownership, showing the data owner and the application owner. The descriptive characteristics, define the names, types and lengths, and definitions describing business data or business processes. Rules and policies, will define security, data cleanliness, timelines for data, and relationships. Physical characteristics define the origin or source, and physical location. Like building a logical data model for creating a database, a logical meta model can help identify the metadata requirements for business data. The metadata repository will be centralized, decentralized, or distributed. A centralized design means that there is one database for the metadata repository that stores metadata for all applications business wide. A centralized metadata repository has the same advantages and disadvantages of a centralized database. Easier to manage because all the data is in one database, but the disadvantage is that bottlenecks may occur. A decentralized metadata repository stores metadata in multiple databases, either separated by location and or departments of the business. This makes management of the repository more involved than a centraliz
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Deepti Gurdasani

Deepti Gurdasani is a British-Indian clinical epidemiologist and statistical geneticist who is a senior lecturer in machine learning at the Queen Mary University of London. Her research considers the genetic diversity of African Populations. Throughout the COVID-19 pandemic, Gurdasani has provided the public with her analysis of the evolving situation mainly on the Twitter platform. == Early life and education == Gurdasani was an undergraduate and medical student at the Christian Medical College Vellore at Tamil Nadu Dr. M.G.R. Medical University. After earning her medical degree and qualifying in internal medicine, she moved to the United Kingdom, where she worked toward a research doctorate in genetic epidemiology at Wolfson College, Cambridge. Her doctoral research involved the design of strategies to understand complex diseases in diverse populations. == Research and career == In 2013, Gurdasani joined the Wellcome Sanger Institute as a postdoctoral fellow, where she worked on the genomic diversity of African populations and how this diversity impacts susceptibility to disease. She makes use of dense genotypes and whole genome sequences to better understand how population movements determined genetic structure. In particular, Gurdasani develops machine learning algorithms to large-scale clinical data sets. At the Sanger Gurdasani co-led the African Genome Variation Project and the Uganda Resource Project. Gurdasani moved to Queen Mary University of London in 2019, where she created deep learning approaches for clinical prediction and the identification of novel, genome-based drug targets. During the COVID-19 pandemic Gurdasani has provided public commentary on the pandemic, making use of both Twitter and print media to share information on the evolving situation. She has researched the incidence of long covid in the UK. In 2021 Gurdasani started to write for The Guardian. == Selected publications == Deepti Gurdasani; Tommy Carstensen; Fasil Tekola-Ayele; et al. (3 December 2014). "The African Genome Variation Project shapes medical genetics in Africa". Nature. 517 (7534): 327–332. doi:10.1038/NATURE13997. ISSN 1476-4687. PMC 4297536. PMID 25470054. Wikidata Q34979569. Nisreen A Alwan; Rochelle Ann Burgess; Simon Ashworth; et al. (15 October 2020). "Scientific consensus on the COVID-19 pandemic: we need to act now". The Lancet. doi:10.1016/S0140-6736(20)32153-X. ISSN 0140-6736. PMC 7557300. PMID 33069277. Wikidata Q100697134. Deepti Gurdasani; Inês Barroso; Eleftheria Zeggini; Manjinder S Sandhu (24 June 2019). "Genomics of disease risk in globally diverse populations". Nature Reviews Genetics. 20 (9): 520–535. doi:10.1038/S41576-019-0144-0. ISSN 1471-0056. PMID 31235872. Wikidata Q93000887. (erratum)
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$Markov switching multifractal$

Markov switching multifractal

In financial econometrics (the application of statistical methods to economic data), the Markov-switching multifractal (MSM) is a model of asset returns developed by Laurent E. Calvet and Adlai J. Fisher that incorporates stochastic volatility components of heterogeneous durations. MSM captures the outliers, log-memory-like volatility persistence and power variation of financial returns. In currency and equity series, MSM compares favorably with standard volatility models such as GARCH(1,1) and FIGARCH both in- and out-of-sample. MSM is used by practitioners in the financial industry for different types of forecasts. == MSM specification == The MSM model can be specified in both discrete time and continuous time. === Discrete time === Let P t {\displaystyle P_{t}} denote the price of a financial asset, and let r t = ln ⁡ ( P t / P t − 1 ) {\displaystyle r_{t}=\ln(P_{t}/P_{t-1})} denote the return over two consecutive periods. In MSM, returns are specified as r t = μ + σ ¯ ( M 1 , t M 2 , t . . . M k ¯ , t ) 1 / 2 ϵ t , {\displaystyle r_{t}=\mu +{\bar {\sigma }}(M_{1,t}M_{2,t}...M_{{\bar {k}},t})^{1/2}\epsilon _{t},} where μ {\displaystyle \mu } and σ {\displaystyle \sigma } are constants and { ϵ t {\displaystyle \epsilon _{t}} } are independent standard Gaussians. Volatility is driven by the first-order latent Markov state vector: M t = ( M 1 , t M 2 , t … M k ¯ , t ) ∈ R + k ¯ . {\displaystyle M_{t}=(M_{1,t}M_{2,t}\dots M_{{\bar {k}},t})\in R_{+}^{\bar {k}}.} Given the volatility state M t {\displaystyle M_{t}} , the next-period multiplier M k , t + 1 {\displaystyle M_{k,t+1}} is drawn from a fixed distribution M with probability γ k {\displaystyle \gamma _{k}} , and is otherwise left unchanged. The transition probabilities are specified by γ k = 1 − ( 1 − γ 1 ) ( b k − 1 ) {\displaystyle \gamma _{k}=1-(1-\gamma _{1})^{(b^{k-1})}} . The sequence γ k {\displaystyle \gamma _{k}} is approximately geometric γ k ≈ γ 1 b k − 1 {\displaystyle \gamma _{k}\approx \gamma _{1}b^{k-1}} at low frequency. The marginal distribution M has a unit mean, has a positive support, and is independent of k. ==== Binomial MSM ==== In empirical applications, the distribution M is often a discrete distribution that can take the values m 0 {\displaystyle m_{0}} or 2 − m 0 {\displaystyle 2-m_{0}} with equal probability. The return process r t {\displaystyle r_{t}} is then specified by the parameters θ = ( m 0 , μ , σ ¯ , b , γ 1 ) {\displaystyle \theta =(m_{0},\mu ,{\bar {\sigma }},b,\gamma _{1})} . Note that the number of parameters is the same for all k ¯ > 1 {\displaystyle {\bar {k}}>1} . === Continuous time === MSM is similarly defined in continuous time. The price process follows the diffusion: d P t P t = μ d t + σ ( M t ) d W t , {\displaystyle {\frac {dP_{t}}{P_{t}}}=\mu dt+\sigma (M_{t})\,dW_{t},} where σ ( M t ) = σ ¯ ( M 1 , t … M k ¯ , t ) 1 / 2 {\displaystyle \sigma (M_{t})={\bar {\sigma }}(M_{1,t}\dots M_{{\bar {k}},t})^{1/2}} , W t {\displaystyle W_{t}} is a standard Brownian motion, and μ {\displaystyle \mu } and σ ¯ {\displaystyle {\bar {\sigma }}} are constants. Each component follows the dynamics: The intensities vary geometrically with k: γ k = γ 1 b k − 1 . {\displaystyle \gamma _{k}=\gamma _{1}b^{k-1}.} When the number of components k ¯ {\displaystyle {\bar {k}}} goes to infinity, continuous-time MSM converges to a multifractal diffusion, whose sample paths take a continuum of local Hölder exponents on any finite time interval. == Inference and closed-form likelihood == When M {\displaystyle M} has a discrete distribution, the Markov state vector M t {\displaystyle M_{t}} takes finitely many values m 1 , . . . , m d ∈ R + k ¯ {\displaystyle m^{1},...,m^{d}\in R_{+}^{\bar {k}}} . For instance, there are d = 2 k ¯ {\displaystyle d=2^{\bar {k}}} possible states in binomial MSM. The Markov dynamics are characterized by the transition matrix A = ( a i , j ) 1 ≤ i , j ≤ d {\displaystyle A=(a_{i,j})_{1\leq i,j\leq d}} with components a i , j = P ( M t + 1 = m j | M t = m i ) {\displaystyle a_{i,j}=P\left(M_{t+1}=m^{j}|M_{t}=m^{i}\right)} . Conditional on the volatility state, the return r t {\displaystyle r_{t}} has Gaussian density f ( r t | M t = m i ) = 1 2 π σ 2 ( m i ) exp ⁡ [ − ( r t − μ ) 2 2 σ 2 ( m i ) ] . {\displaystyle f(r_{t}|M_{t}=m^{i})={\frac {1}{\sqrt {2\pi \sigma ^{2}(m^{i})}}}\exp \left[-{\frac {(r_{t}-\mu )^{2}}{2\sigma ^{2}(m^{i})}}\right].} === Conditional distribution === === Closed-form Likelihood === The log likelihood function has the following analytical expression: ln ⁡ L ( r 1 , … , r T ; θ ) = ∑ t = 1 T ln ⁡ [ ω ( r t ) . ( Π t − 1 A ) ] . {\displaystyle \ln L(r_{1},\dots ,r_{T};\theta )=\sum _{t=1}^{T}\ln[\omega (r_{t}).(\Pi _{t-1}A)].} Maximum likelihood provides reasonably precise estimates in finite samples. === Other estimation methods === When M {\displaystyle M} has a continuous distribution, estimation can proceed by simulated method of moments, or simulated likelihood via a particle filter. == Forecasting == Given r 1 , … , r t {\displaystyle r_{1},\dots ,r_{t}} , the conditional distribution of the latent state vector at date t + n {\displaystyle t+n} is given by: Π ^ t , n = Π t A n . {\displaystyle {\hat {\Pi }}_{t,n}=\Pi _{t}A^{n}.\,} MSM often provides better volatility forecasts than some of the best traditional models both in and out of sample. Calvet and Fisher report considerable gains in exchange rate volatility forecasts at horizons of 10 to 50 days as compared with GARCH(1,1), Markov-Switching GARCH, and Fractionally Integrated GARCH. Lux obtains similar results using linear predictions. == Applications == === Multiple assets and value-at-risk === Extensions of MSM to multiple assets provide reliable estimates of the value-at-risk in a portfolio of securities. === Asset pricing === In financial economics, MSM has been used to analyze the pricing implications of multifrequency risk. The models have had some success in explaining the excess volatility of stock returns compared to fundamentals and the negative skewness of equity returns. They have also been used to generate multifractal jump-diffusions. == Related approaches == MSM is a stochastic volatility model with arbitrarily many frequencies. MSM builds on the convenience of regime-switching models, which were advanced in economics and finance by James D. Hamilton. MSM is closely related to the Multifractal Model of Asset Returns. MSM improves on the MMAR's combinatorial construction by randomizing arrival times, guaranteeing a strictly stationary process. MSM provides a pure regime-switching formulation of multifractal measures, which were pioneered by Benoit Mandelbrot.
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Bidyut Baran Chaudhuri

Bidyut Baran Chaudhuri (B. B. Chauduri) is a senior computer scientist and an emeritus professor of Techno India University in West Bengal, India. He is also adjuncted to Indian Statistical Institute, where he was a professor for about three decades. He was the founding Head of Computer Vision and Pattern Recognition Unit (which was established in 1994) of ISI. Moreover, he was a J.C. Bose Fellow and Indian National Academy of Engineering Distinguished Professor at ISI. He was the vice-president of the Society for Natural Language Technology Research (SNLTR). His primary research contributes to the fields of computer vision, image processing and pattern recognition. He is a pioneer of "Indian language script OCR". == Education == Chaudhuri received his BSc (Hons.), BTech and MTech degrees from University of Calcutta, India in 1969, 1972 and 1974, respectively and PhD Degree from Indian Institute of Technology Kanpur in 1980. He did his post-doc work during 1981-1982 from Queen's University, U.K, through Leverhulme Overseas Fellowship. He also worked as a visiting faculty at Tech University, Hannover during 1986-87 as well as at GSF Institute of Radiation Protection (now Leibnitz Institute), Munich in 1990 and 1992. == Awards and recognition == Chaudhuri has been elected as a Life Fellow of IEEE "for contributions to pattern recognition, especially Indian language script OCR, document processing and natural language processing". He has become a Fellow of International Association for Pattern Recognition (IAPR) "for contributions to character recognition and speech synthesis in Indian language". He is also Fellow of The World Academy of Sciences (TWAS), Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), National Academy of Sciences (NASI), and Institute of Electronics and Telecommunication Engineering (IETE). In 2011, Chaudhuri received the Om Prakash Bhasin Award for his contribution in the field of electronics and information technology. Chaudhuri's interview on some of his works has been reported in Indian newspaper as well. He is within world's top 2% scientists and top-10 Indian AI scientists according to a study conducted by Stanford University. He has also been featured as top-10 machine learning researcher from India.
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Shell Control Box

Shell Control Box (SCB) is a network security appliance that controls privileged access to remote IT systems, records activities in replayable audit trails, and prevents malicious actions. For example, it records as a system administrator updates a file server or a third-party network operator configures a router. The recorded audit trails can be replayed like a movie to review the events as they occurred. The content of the audit trails is indexed to make searching for events and automatic reporting possible. SCB is a Linux-based device developed by Balabit. It is an application level proxy gateway. In 2017, Balabit changed the name of the product to Privileged Session Management (PSM) and repositioned it as the core module of its Privileged Access Management solution. == Main Features == Balabit’s Privileged Session Management (PSM), Shell Control Box (SCB) is a device that controls, monitors, and audits remote administrative access to servers and network devices. It is a tool to oversee system administrators by controlling the encrypted connections used for administration. PSM (SCB) has full control over the SSH, RDP, Telnet, TN3270, TN5250, Citrix ICA, and VNC connections, providing a framework (with solid boundaries) for the work of the administrators. === Gateway Authentication === PSM (SCB) acts as an authentication gateway, enforcing strong authentication before users access IT assets. PSM can also integrate to user directories (for example, a Microsoft Active Directory) to resolve the group memberships of the users who access the protected servers. Credentials for accessing the server are retrieved transparently from PSM’s credential store or a third-party password management system by PSM impersonating the authenticated user. This automatic password retrieval protects the confidentiality of passwords as users can never access them. === Access Control === PSM controls and audits privileged access over the most wide-spread protocols such as SSH, RDP, or HTTP(s). The detailed access management helps to control who can access what and when on servers. It is also possible to control advanced features of the protocols, like the type of channels permitted. For example, unneeded channels like file transfer or file sharing can be disabled, reducing the security risk on the server. With PSM policies for privileged access can be enforced in one single system. === 4-eyes Authorization === To avoid accidental misconfiguration and other human errors, PSM supports the 4-eyes authorization principle. This is achieved by requiring an authorizer to allow administrators to access the server. The authorizer also has the possibility to monitor – and terminate - the session of the administrator in real-time, as if they were watching the same screen. === Real-time Monitoring and Session Termination === PSM can monitor the network traffic in real time, and execute various actions if a certain pattern (for example, a suspicious command, window title or text) appears on the screen. PSM can also detect specific patterns such as credit card numbers. In case of detecting a suspicious user action, PSM can send an e-mail alert or immediately terminate the connection. For example, PSM can block the connection before a destructive administrator command, such as the „rm” comes into effect. === Session Recording === PSM makes user activities traceable by recording them in tamper-proof and confidential audit trails. It records the selected sessions into encrypted, timestamped, and digitally signed audit trails. Audit trails can be browsed online, or followed real-time to monitor the activities of the users. PSM replays the recorded sessions just like a movie – actions of the users can be seen exactly as they appeared on their monitor. The Balabit Desktop Player enables fast forwarding during replays, searching for events (for example, typed commands or pressing Enter) and texts seen by the user. In the case of any problems (database manipulation, unexpected shutdown, etc.) the circumstances of the event are readily available in the trails, thus the cause of the incident can be identified. In addition to recording audit trails, transferred files can be also recorded and extracted for further analysis.
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Quotient automaton

In computer science, in particular in formal language theory, a quotient automaton can be obtained from a given nondeterministic finite automaton by joining some of its states. The quotient recognizes a superset of the given automaton; in some cases, handled by the Myhill–Nerode theorem, both languages are equal. == Formal definition == A (nondeterministic) finite automaton is a quintuple A = ⟨Σ, S, s0, δ, Sf⟩, where: Σ is the input alphabet (a finite, non-empty set of symbols), S is a finite, non-empty set of states, s0 is the initial state, an element of S, δ is the state-transition relation: δ ⊆ S × Σ × S, and Sf is the set of final states, a (possibly empty) subset of S. A string a1...an ∈ Σ is recognized by A if there exist states s1, ..., sn ∈ S such that ⟨si-1,ai,si⟩ ∈ δ for i=1,...,n, and sn ∈ Sf. The set of all strings recognized by A is called the language recognized by A; it is denoted as L(A). For an equivalence relation ≈ on the set S of A’s states, the quotient automaton A/≈ = ⟨Σ, S/≈, [s0], δ/≈, Sf/≈⟩ is defined by the input alphabet Σ being the same as that of A, the state set S/≈ being the set of all equivalence classes of states from S, the start state [s0] being the equivalence class of A’s start state, the state-transition relation δ/≈ being defined by δ/≈([s],a,[t]) if δ(s,a,t) for some s ∈ [s] and t ∈ [t], and the set of final states Sf/≈ being the set of all equivalence classes of final states from Sf. The process of computing A/≈ is also called factoring A by ≈. == Example == For example, the automaton A shown in the first row of the table is formally defined by ΣA = {0,1}, SA = {a,b,c,d}, sA0 = a, δA = { ⟨a,1,b⟩, ⟨b,0,c⟩, ⟨c,0,d⟩ }, and SAf = { b,c,d }. It recognizes the finite set of strings { 1, 10, 100 }; this set can also be denoted by the regular expression "1+10+100". The relation (≈) = { ⟨a,a⟩, ⟨a,b⟩, ⟨b,a⟩, ⟨b,b⟩, ⟨c,c⟩, ⟨c,d⟩, ⟨d,c⟩, ⟨d,d⟩ }, more briefly denoted as a≈b,c≈d, is an equivalence relation on the set {a,b,c,d} of automaton A’s states. Building the quotient of A by that relation results in automaton C in the third table row; it is formally defined by ΣC = {0,1}, SC = {a,c}, sC0 = a, δC = { ⟨a,1,a⟩, ⟨a,0,c⟩, ⟨c,0,c⟩ }, and SCf = { a,c }. It recognizes the finite set of all strings composed of arbitrarily many 1s, followed by arbitrarily many 0s, i.e. { ε, 1, 10, 100, 1000, ..., 11, 110, 1100, 11000, ..., 111, ... }; this set can also be denoted by the regular expression "10". Informally, C can be thought of resulting from A by glueing state a onto state b, and glueing state c onto state d. The table shows some more quotient relations, such as B = A/a≈b, and D = C/a≈c. == Properties == For every automaton A and every equivalence relation ≈ on its states set, L(A/≈) is a superset of (or equal to) L(A). Given a finite automaton A over some alphabet Σ, an equivalence relation ≈ can be defined on Σ by x ≈ y if ∀ z ∈ Σ: xz ∈ L(A) ↔ yz ∈ L(A). By the Myhill–Nerode theorem, A/≈ is a deterministic automaton that recognizes the same language as A. As a consequence, the quotient of A by every refinement of ≈ also recognizes the same language as A.
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Top 10 AI Chatbots Compared (2026)

Shopping for the best AI chatbot? An AI chatbot is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI chatbot slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Top 10 AI Video Generators Compared (2026)

Shopping for the best AI video generator? An AI video generator is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI video generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Trigger list

Trigger list in its most general meaning refers to a list whose items are used to initiate ("trigger") certain actions. == United States: Private financial information == In the United States, when a person applies for a mortgage loan, the lender makes a credit inquiry about the potential borrower from the national credit bureaus, Equifax, Experian and TransUnion. Unless the borrower is opted out, the credit bureaus put the applicants onto a "trigger list" of "leads" about persons who are interested in new loans. These lists are sold to numerous lenders all over the United States, and soon after the application the applicant starts receiving offers from all parts of the country. The trigger lists contain a significant amount of personal financial information. Among the buyers of trigger lists are "lead generators" which resell filtered information to borrowers, e.g., of people who live in a certain area and have a certain credit score. While the Federal Trade Commission considers the market of "trigger lists" to be a legal business, many people and organizations (such as the National Association of Mortgage Brokers) consider this a serious breach of privacy and lobby for putting this practice under regulatory controls. As of now, American consumers may opt-out from "trigger lists" by calling 1-888-5-OPTOUT (1-888-567-8688). == Nuclear non-proliferation == The Zangger Committee and the Nuclear Suppliers Group maintain lists of items that may contribute to nuclear proliferation; The nuclear non-proliferation treaty forbids its members to export such items to non-treaty members. these items are said to trigger the countries' responsibilities under the NPT, hence the name.
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Ziad Obermeyer

Ziad Obermeyer (Arabic: زياد أوبرماير) is a Lebanese American physician and researcher whose work focuses on machine learning, health policy, and clinical decision-making in medicine. He is the Blue Cross of California Distinguished Associate Professor at the UC Berkeley School of Public Health, a Chan Zuckerberg Biohub investigator, and a research associate at the National Bureau of Economic Research. He is known for his research on racial bias in health care algorithms and the use of artificial intelligence in health care. == Early life and education == Obermeyer was born in Beirut, Lebanon, and raised in Cambridge, Massachusetts. He earned a Bachelor of Arts degree from Harvard College and a Master of Philosophy (M.Phil.) in History and Science from the University of Cambridge. He received his Doctor of Medicine (M.D.) from Harvard Medical School in 2008. Before pursuing medicine, Obermeyer worked as a consultant at McKinsey & Company, advising pharmaceutical and global health clients in New Jersey, Geneva, and Tokyo. After completing his medical degree, he trained as an emergency physician at Mass General Brigham (MGB) in Boston, Massachusetts. He later continued practicing emergency medicine at the Fort Defiance Indian Hospital on the Navajo Nation in Arizona. == Academic career == Obermeyer served as an Assistant Professor at Harvard Medical School from 2014 to 2020. In 2020, he joined the University of California, Berkeley as an Associate Professor and the Blue Cross of California Distinguished Professor at the School of Public Health. == Research focus == === Algorithmic racial bias in healthcare === In 2019, Obermeyer and economist Sendhil Mullainathan examined a commercial healthcare algorithm by UnitedHealth Group, used in hospitals and by insurers to identify patients with complex health needs. The study found that the algorithm underestimated the health needs of Black patients compared to white patients with similar conditions and that reformulating it would reduce racial bias. In 2020, Obermeyer analyzed an algorithm used to allocate CARE Act relief funding to hospitals. The study identified allocation patterns that favored hospitals with higher revenues over hospitals serving larger numbers of COVID-19 patients who are predominantly Black. === Clinical decision-making === In 2021, Obermeyer and colleagues examined physician decision-making in cardiac care using machine learning models. The study found that physicians misdiagnose cases when they rely on symptoms representative of a heart attack, such as chest pain, over other symptoms. === Pain assessment === Obermeyer developed a deep learning approach to investigate the severity of osteoarthritis in underserved communities. == Policy and regulatory work == Following the publication of the 2019 algorithmic racial bias study, the New York Department of Financial Services and Department of Health launched an investigation into UnitedHealth Group's algorithm, requesting that the company cease using it, citing discriminatory business practices. Also related to this study, in December 2019, Democratic Senators Cory Booker and Ron Wyden released letters to the Federal Trade Commission and Centers for Medicare and Medicaid Services asking to investigate potential discrimination in decision-making algorithms against marginalized communities in healthcare. The senators also wrote to major healthcare companies, including Aetna and Blue Cross Blue Shield, about their internal safeguards against racial bias in their technology. In 2021, Obermeyer and colleagues at the University of Chicago Booth School of Business released the Algorithmic Bias Playbook, a resource for policymakers and technical teams working in healthcare on how to measure and mitigate algorithmic racial bias. Obermeyer testified before the U.S. Senate Financial Committee in February 2024 on artificial intelligence in healthcare, recommending transparency requirements for AI developers and independent algorithm evaluations. In December 2025, he testified before the United States House Committee on Oversight and Government Reform on the role of AI in affordable healthcare and the impact of its integration on the workforce. == Organizations == In 2021, Obermeyer cofounded Nightingale Open Science, a non-profit that creates new medical imaging datasets available for research, and Dandelion Health, a health data analytics company. In June 2023, the company launched a program to audit and evaluate the performance of algorithms to identify potential racial, ethnic, and geographic bias, funded by the Gordon and Betty Moore Foundation and the SCAN Foundation. Dandelion Health partnered with the American Heart Association in 2025 to power an AI assessment lab for cardiovascular algorithms. Obermeyer is a founding faculty member of the University of California, Berkeley–University of California, San Francisco joint program in computational precision health. == Recognition == TIME magazine named Obermeyer one of the 100 most influential people in artificial intelligence in 2023. He has served as a Chan Zuckerberg Biohub Investigator since 2022, and as a Research Associate at the National Bureau of Economic Research since 2023. He was designated an Emerging Leader by the National Academy of Medicine in 2020. Obermeyer's racial bias study received the Willard G. Manning Memorial Award for the Best Research in Health Econometrics from the American Society of Health Economists (ASHEcon) in 2021 and the Responsible Business Education Award from the Financial Times in 2022.
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Tamara Berg

Tamara Lee Berg is a tenured associate professor at the University of North Carolina at Chapel Hill and a research scientist manager at Facebook AML/FAIR. == Education == Berg obtained her PhD in computer science from the University of California, Berkeley in 2007 as a member of the Berkeley Computer Vision Group. She was an assistant professor at Stony Brook University from 2008 to 2013 before joining University of North Carolina Chapel Hill in 2013. == Research == Berg's research interests are at the boundary of computer vision and natural language processing. In particular, she focuses on understanding the connections between vision and language, for example, to automatically identify people in news photographs, for generating natural language descriptions for images, or for recognising clothing and style. == Selected awards and honours == 2019 Mark Everingham Prize 2013 Marr Prize at the International Conference on Computer Vision 2011 National Science Foundation Career Award == Personal life == Berg is married to fellow computer vision researcher Alexander Berg.
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