Project Maven (officially Algorithmic Warfare Cross Functional Team) is a United States Department of Defense initiative launched in 2017 to accelerate the adoption of machine learning and data integration across U.S. military intelligence workflows, specifically in intelligence, surveillance, target acquisition, and reconnaissance as well as in geospatial intelligence. It initially focused on applying computer vision for processing images and videos for intelligence purposes. Currently, the program operates under the National Geospatial-Intelligence Agency (NGA) and encompasses multiple applications across the Department of Defense spanning military operation targeting support, data integration and visualization for analysts, and training machine learning models on labeled datasets of military assets and infrastructure. It integrates data from drones, satellites, and other sensors to flag potential targets, present findings to human analysts, and relay their decisions to operational systems. The program originated under Deputy Secretary Robert O. Work after he raised concerns about China's advances in defense applications of artificial intelligence. Project leaders, Colonel Drew Cukor, USMC, and Lt. Gen. Jack Shanahan, framed the program as human-in-the-loop decision support inside the Department of Defense rather than as an autonomous weapons platform. Contractors supporting Maven have included Google, which withdrew in 2018 after internal protests, and follow-on integrators such as Palantir, Anduril, Amazon Web Services, and Anthropic (withdrew in 2026). The Pentagon credits Maven with providing 2024 targeting support for U.S. airstrikes in Iraq, Syria, and Yemen, along with locating hostile maritime assets in the Red Sea. == Administrative history == Initially, the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017 proposing an "Algorithmic Warfare Cross-Functional Team". With the help of Defense Innovation Unit, the project obtained the support of top talents in AI outside of the traditional defense contracting base. It was initially funded for $70 million. Jack Shanahan was the director of the project during April 2017 to December 2018. At the second Defense One Tech Summit in July 2017, Cukor said that the investment in a "deliberate workflow process" was funded by the Department [of Defense] through its "rapid acquisition authorities" for about "the next 36 months". In the defense industry, the standard procedure for the military to acquire hardware is by way of research, development, test, and evaluation (RDT&E), followed by production and sustainment. In 2017, acquiring software was done in the same way as hardware. This created a problem, since software is constantly updated. Project Maven procured software using Broad Agency Announcements, a flexible contracting vehicle that categorized software as consistently RDT&E, allowing constant updating. Another issue was that the government usually acquired the intellectual property (IP) for procured software, and with the project, only parts of the IP of the software was acquired. Cukor used the principle of "platform IP belongs to the vendor, configurations on top are the customer's". For example, Palantir retained IP to their core platform, while the government obtained the IP to Maven-specific logic configured on top of it. According to US Air Force Lt. Gen. Jack Shanahan in November 2017, it is "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the ability to identify adversaries by harvesting data from sensors on UAVs and satellites. As of 2017 December, 150,000 images had been manually labelled to establish the first training data sets, and it was projected to reach one million by January 2018. Project Maven was funded for $221 million in fiscal 2020. In 2020, the House and Senate conferees on the National Defense Authorization Act for Fiscal Year 2021, agreed to the Senate's recommendation to fund the Pentagon's $250 million request for Project Maven. At the GEOINT Symposium of 2022, it was announced that Project Maven was transferred from the Office of the Under Secretary of Defense for Intelligence and Security to the NGA, under President Biden’s proposed budget for Fiscal Year 2023. It became a Program of Record on 2023 November 7. Frank "Trey" Whitworth, vice admiral, was the director of NGA from June 2022 to November 2025. Whitworth was initially skeptical of the program, suspecting it was incautious about the targeting principles, but later regarded it as "important work". As of 2024, the project is jointly administered by the NGA and the CDAO, and its director is Rachel Martin. Before 2025, Biden appointees within CDAO had held back AI development for safety and reliability concerns, though as of 2025, this has stopped. As of 2024, Maven provided the cloud infrastructure, software capabilities, and AI for CDAO's Combined Joint All-Domain Command and Control initiatives. As of summer 2025, there were eight Maven initiatives. Of these, five were in the NGA, including analyzing drone feeds and satellite imagery. On 18 September 2025, the UK government announced a new partnership with Palantir to develop AI-powered military capabilities for decision-making and targeting, identifying opportunities worth up to £750 million over five years. On 25 March 2025, the NATO Communications and Information Agency and Palantir finalized the acquisition of the Palantir Maven Smart System NATO (MSS NATO) for employment within NATO's Allied Command Operations. It was planned to be used within 30 days of acquisition. In a letter to Pentagon on 9 March 2026, Steve Feinberg stated that Project Maven will become an official program of record by September 2026, the close of the current fiscal year. The project would transfer from the NGA to the CDAO within 30 days. Future contracting with Palantir would be handled by the US Army. In 2026-03, it was announced that the US Army Combined Arms Command would integrate Maven into its training. == Technology == Project Maven uses machine learning algorithms to analyze and fuse vast amounts of surveillance data from multiple sources made possible through data integration using Palantir Technologies. The data sources include photographs, satellite imagery, geolocation data (IP address, geotag, metadata, etc) from communications intercepts, infrared sensors, synthetic-aperture radar, and more. The system is mainly used for assisting analysts in intelligence, surveillance, target acquisition, and reconnaissance. Machine learning systems, including object recognition systems, process the data and identify potential targets, such as enemy tanks or location of new military facility. The training dataset included at least 4 million images of military objects such as warships, labelled by humans. The user interface is called Maven Smart System. It could display information such as aircraft movements, logistics, locations of key personnel, locations on the no-strike list, ships, etc. Yellow-outlined boxes show potential targets. Blue-outlined boxes show friendly forces or no-strike zones. It could also transmit, directly to weapons, a human decision to fire weapons. Internal documentation referred to "Maven ATR: automatic target recognition". Initially the project focused on applications of computer vision. The project's leaders were particularly impressed by model performance on ImageNet. As of 2018, the purpose of the system was AI-enabled analysis of full-motion video. In 2022 it expanded to combatant commands under the AI and Data Acceleration Initiative. In 2022, it was reported that the project expanded to non-image data, including captured enemy material, maritime intelligence, and publicly available information. In 2024, it was stated that Maven's key technical contribution was data management: Maven standardizes heterogeneous data through an ontology layer so data can be fused, exchanged across cloud and edge systems, and used by multiple applications. The system was presented as a broader data-centric warfighting system that feeds apps for planning, preparing, and executing operations. In 2024, the Broad Area Surveillance-Targeting (BAS-T) is a part of Maven. The system detects objects in images and uses data fusion to produce a common operational picture containing "priority based, in-depth assessment of the enemy systems pre
Flektor
Flektor was a web application that allowed users the ability to create and "mashup" their own content (photos, videos, music, etc.) and share it via email, on social networking websites MySpace, Facebook, Blogger, Digg, eBay or on personal blogs. The company's website (Flektor.com) launched on April 2, 2007, and over 40,000 people began utilizing its features just one month later. Flektor closed down in January 2009. Flektor offered tools and widgets that included audio, video, photos, text, and approximately 100 effects, transitions and filters to be used with media. Users could create personalized slideshows, polls, postcards, and streaming video projects which the website calls "fleks". Flektor also offered Chat (used as a MySpace addon) and Movie Editor, which provided the ability to edit content and assets together. Users of Flektor could import media from websites like Photobucket and Google's YouTube, and then edit their content with the site's editing tools. Flektor's erstwhile competitors include Slide.com (founded by PayPal co-founder Max Levchin), RockYou!, Yahoo's JumpCut and Brightcove. == History == Flektor was created by Jason Rubin, Andy Gavin and former HBO executive Jason R. Kay. Both Rubin and Gavin spent most of their careers in the video game industry developing games for publishers like Electronic Arts, Universal Interactive Studios and Sony Computer Entertainment America. They founded a successful game development studio called Naughty Dog and were responsible for games such as Crash Bandicoot and Jak and Daxter. After selling Naughty Dog to Sony, Rubin focused on a comic book series called Iron and the Maiden before teaming up again with Gavin to venture into the web industry with Flektor. Jason Kay spent four years at Home Box Office, working as a consultant to the EVP of Business Development. They recruited former employee and then Naughty Dog Lead Programmer Scott Shumaker to lead the technology team along with Gavin. Ryan Evans joined shortly thereafter, spearheading product development. Flektor is based in Culver City, California. In May 2007, the company was sold to Fox Interactive Media, which is a division of News Corp., for more than $20 million. The deal coincided with Fox's acquisition of Photobucket, an image-hosting and sharing website. Fox Interactive Media already holds possession of MySpace, IGN Entertainment, FOXSports.com, AmericanIdol.com and Rotten Tomatoes. After the acquisition, Rubin, Gavin and Kay departed, leaving the studio in the hands of Shumaker and Evans. In the fall of 2007, Flektor partnered with its sister company, MySpace, and MTV to provide instant audience feedback via polls for the interactive MySpace/ MTV Presidential Dialogues series with presidential candidates Senator Barack Obama, Senator John McCain and John Edwards. Use of Flektor's polling system, enabled hosts John McLaughlin and Geoffrey Garin to cater their questions towards subjects of voter-interest. In the fall of 2008, Flektor built the official site for the 2008 Presidential debates, hosted at MyDebates. In January 2009, due to a company directive to focus on the core MySpace property, Fox Interactive announced that Flektor would be shut down, with some of its technology being incorporated into MySpace.
Scale space
Scale-space theory is a framework for multi-scale signal representation developed by the computer vision, image processing and signal processing communities with complementary motivations from physics and biological vision. It is a formal theory for handling image structures at different scales, by representing an image as a one-parameter family of smoothed images, the scale-space representation, parametrized by the size of the smoothing kernel used for suppressing fine-scale structures. The parameter t {\displaystyle t} in this family is referred to as the scale parameter, with the interpretation that image structures of spatial size smaller than about t {\displaystyle {\sqrt {t}}} have largely been smoothed away in the scale-space level at scale t {\displaystyle t} . The main type of scale space is the linear (Gaussian) scale space, which has wide applicability as well as the attractive property of being possible to derive from a small set of scale-space axioms. The corresponding scale-space framework encompasses a theory for Gaussian derivative operators, which can be used as a basis for expressing a large class of visual operations for computerized systems that process visual information. This framework also allows visual operations to be made scale invariant, which is necessary for dealing with the size variations that may occur in image data, because real-world objects may be of different sizes and in addition the distance between the object and the camera may be unknown and may vary depending on the circumstances. == Definition == The notion of scale space applies to signals of arbitrary numbers of variables. The most common case in the literature applies to two-dimensional images, which is what is presented here. Consider a given image f {\displaystyle f} where f ( x , y ) {\displaystyle f(x,y)} is the greyscale value of the pixel at position ( x , y ) {\displaystyle (x,y)} . The linear (Gaussian) scale-space representation of f {\displaystyle f} is a family of derived signals L ( x , y ; t ) {\displaystyle L(x,y;t)} defined by the convolution of f ( x , y ) {\displaystyle f(x,y)} with the two-dimensional Gaussian kernel g ( x , y ; t ) = 1 2 π t e − ( x 2 + y 2 ) / 2 t {\displaystyle g(x,y;t)={\frac {1}{2\pi t}}e^{-(x^{2}+y^{2})/2t}\,} such that L ( ⋅ , ⋅ ; t ) = g ( ⋅ , ⋅ ; t ) ∗ f ( ⋅ , ⋅ ) , {\displaystyle L(\cdot ,\cdot ;t)\ =g(\cdot ,\cdot ;t)f(\cdot ,\cdot ),} where the semicolon in the argument of L {\displaystyle L} implies that the convolution is performed only over the variables x , y {\displaystyle x,y} , while the scale parameter t {\displaystyle t} after the semicolon just indicates which scale level is being defined. This definition of L {\displaystyle L} works for a continuum of scales t ≥ 0 {\displaystyle t\geq 0} , but typically only a finite discrete set of levels in the scale-space representation would be actually considered. The scale parameter t = σ 2 {\displaystyle t=\sigma ^{2}} is the variance of the Gaussian filter and as a limit for t = 0 {\displaystyle t=0} the filter g {\displaystyle g} becomes an impulse function such that L ( x , y ; 0 ) = f ( x , y ) , {\displaystyle L(x,y;0)=f(x,y),} that is, the scale-space representation at scale level t = 0 {\displaystyle t=0} is the image f {\displaystyle f} itself. As t {\displaystyle t} increases, L {\displaystyle L} is the result of smoothing f {\displaystyle f} with a larger and larger filter, thereby removing more and more of the details that the image contains. Since the standard deviation of the filter is σ = t {\displaystyle \sigma ={\sqrt {t}}} , details that are significantly smaller than this value are to a large extent removed from the image at scale parameter t {\displaystyle t} , see the following figures and for graphical illustrations. === Why a Gaussian filter? === When faced with the task of generating a multi-scale representation one may ask: could any filter g of low-pass type and with a parameter t which determines its width be used to generate a scale space? The answer is no, as it is of crucial importance that the smoothing filter does not introduce new spurious structures at coarse scales that do not correspond to simplifications of corresponding structures at finer scales. In the scale-space literature, a number of different ways have been expressed to formulate this criterion in precise mathematical terms. The conclusion from several different axiomatic derivations that have been presented is that the Gaussian scale space constitutes the canonical way to generate a linear scale space, based on the essential requirement that new structures must not be created when going from a fine scale to any coarser scale. Conditions, referred to as scale-space axioms, that have been used for deriving the uniqueness of the Gaussian kernel include linearity, shift invariance, semi-group structure, non-enhancement of local extrema, scale invariance and rotational invariance. In the works, the uniqueness claimed in the arguments based on scale invariance has been criticized, and alternative self-similar scale-space kernels have been proposed. The Gaussian kernel is, however, a unique choice according to the scale-space axiomatics based on causality or non-enhancement of local extrema. === Alternative definition === Equivalently, the scale-space family can be defined as the solution of the diffusion equation (for example in terms of the heat equation), ∂ t L = 1 2 ∇ 2 L , {\displaystyle \partial _{t}L={\frac {1}{2}}\nabla ^{2}L,} with initial condition L ( x , y ; 0 ) = f ( x , y ) {\displaystyle L(x,y;0)=f(x,y)} . This formulation of the scale-space representation L means that it is possible to interpret the intensity values of the image f as a "temperature distribution" in the image plane and that the process that generates the scale-space representation as a function of t corresponds to heat diffusion in the image plane over time t (assuming the thermal conductivity of the material equal to the arbitrarily chosen constant 1/2). Although this connection may appear superficial for a reader not familiar with differential equations, it is indeed the case that the main scale-space formulation in terms of non-enhancement of local extrema is expressed in terms of a sign condition on partial derivatives in the 2+1-D volume generated by the scale space, thus within the framework of partial differential equations. Furthermore, a detailed analysis of the discrete case shows that the diffusion equation provides a unifying link between continuous and discrete scale spaces, which also generalizes to nonlinear scale spaces, for example, using anisotropic diffusion. Hence, one may say that the primary way to generate a scale space is by the diffusion equation, and that the Gaussian kernel arises as the Green's function of this specific partial differential equation. == Motivations == The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. This implies that real-world objects, in contrast to idealized mathematical entities such as points or lines, may appear in different ways depending on the scale of observation. For example, the concept of a "tree" is appropriate at the scale of meters, while concepts such as leaves and molecules are more appropriate at finer scales. For a computer vision system analysing an unknown scene, there is no way to know a priori what scales are appropriate for describing the interesting structures in the image data. Hence, the only reasonable approach is to consider descriptions at multiple scales in order to be able to capture the unknown scale variations that may occur. Taken to the limit, a scale-space representation considers representations at all scales. Another motivation to the scale-space concept originates from the process of performing a physical measurement on real-world data. In order to extract any information from a measurement process, one has to apply operators of non-infinitesimal size to the data. In many branches of computer science and applied mathematics, the size of the measurement operator is disregarded in the theoretical modelling of a problem. The scale-space theory on the other hand explicitly incorporates the need for a non-infinitesimal size of the image operators as an integral part of any measurement as well as any other operation that depends on a real-world measurement. There is a close link between scale-space theory and biological vision. Many scale-space operations show a high degree of similarity with receptive field profiles recorded from the mammalian retina and the first stages in the visual cortex. In these respects, the scale-space framework can be seen as a theoretically well-founded paradigm for early vision, which in addition has been thoroughly tested by algorithms and experiments. == Gaussian derivatives == At any scale in scale space, we c
Nanosemantics
Nanosemantics Lab is a Russian IT company specializing in natural language processing (NLP), computer vision (CV), speech technologies (ASR/TTS) and creation of interactive dialog interfaces, particularly chatbots and virtual assistants, based on artificial intelligence (AI). The company uses neural network platforms, including its own-made platform PuzzleLib which works on Russian-made microprocessor architecture Elbrus and Russia-based Astra Linux operating system. The company was founded in 2005 by Igor Ashmanov and Natalya Kaspersky. == Profile == The company was one of the first on Russian market to develop dialog interfaces for different branches of businesses, as well as to support community of AI developers. The company's most demanded product, as for beginning of the 2020s, is the automated "online advisers", functioning as chat bots, made for helping customers with usage of commercial products. In 2009 the company released an online service called iii.ru, where visitors were able to create their own AI-based virtual personalities entitles "infs" (for free). A visitor was able to train its own "inf" and let them chat to other "live" visitors as well with other "infs". More than 2.3 million of "infs" were created and trained by visitors over several years. Nanosemantics Lab maintains its own linguistic programming language for AI development called Dialog Language (DL). Popular social networks and instant messaging services may be used as base platforms. Nanosemantics' AI bots support different types of businesses: banks and financial services, telecommunications, retail, travel and automobile industry, home appliances production, etc. Among its solutions, Nanosemantics lists projects for various companies and institutions, among them VTB, Beeline, MTS, Sberbank, Higher School of Economics, Webmoney, Gazpromneft, Rostelecom, Ford Motors, Ministry of Health of the Russian Federation and others. The company uses the term "inf" for naming its numerous types of chat bots. The term was coined by co-founder Igor Ashmanov, head of Ashmanov & Partners. A 2014 scholarly research at Higher School of Economics, called "Basics of Business Informatics", states that such "infs", when used at business, may lower load on employees, collect statistics useful for understanding market demand and also may increase customer loyalty by providing fast and informative answers due to usage of large databases. The same research describes Nanosemantics' project for Russian branch of Ford Motors company, when AI capabilities were used for promoting the car model Ford Kuga. The research pointed out that within 2 months since beginning, the promo-website conducted 47774 talks of visitors with the specialized "inf", which indicated several hundred thousand of questions and the longest chat lasted for 3 hours 10 minutes. One-year promo campaign showed that 28.6% of people who made pre-orders talked to an "inf". In 2016 Nanosemantics launched a SaaS platform aimed at creating customized virtual assistants by users. The company's flagship product is considered to be Dialog Operating System (DialogOS), a professional corporate platform for creating intellectual voice and textual bots. It has its own linguistic programming language for creation of flexible scenarios and ready-studied neural natural language processing modules that are able to understand human interlocutors. In 2021 the company presented technology called NLab Speech ASR which contains a set of neural-networking algorithms for processing audio signals and analysis of texts that were trained and calibrated using speech-based big data marked up manually. The technology allows speed of processing of data up to "6 real-time factor" and precision values in noisy audio data may exceed 82%. In March 2022 the technology was included in Russia's Joint Registry for Russian Programs for Computers and Databases. As well, another technology was included: NLab Speech TTS, which is text-to-speech system that produces synthesized speech from printed text. == Joint projects == Nanosemantics participates in Ashmanov & Partners' projects related to AI. Since 2014, it helps in development of hardware "personal assistant" called Lexy, a solution similar to Amazon Alexa and the analogues. In August 2019 it was announced that Nanosemantics is going to participate in creation of open operating system for creating automated voice assistants. The project was called SOVA (Smart Open Virtual Assistant) and received investment of 300 million roubles (~$4,6 million) from Russian state-maintained National Technological Initiative. The company maintains long-term partnerships with Skolkovo Innovation Center (resident of IT cluster), branch association "Neuronet" and Yandex. Together with USA-based startup Remedy Logic, Nanosemantics has developed a medical diagnostic system for finding, using AI, spinal pathologies in tomography images of human bodies. Among them: central, foraminal and lateral lumbar stenosis, hernias, arthrosis. The system offers options of treatment. Since August 2021 the company is the resident of Technology Valley of Moscow State University. Also in 2021, Nanosemantics became a member of Committee on Artificial Intelligence within the Russian Association of Software Developers "Native Soft". The company states as one of its missions support of initiatives aimed at preservation and development of the Russian language. In May 2021, together with Pushkin Institute, the company created a chat bot called Phil, that explains to Russian people meaning of different Russian neologisms, and offers synonyms for them. Bot's vocabulary contains more than 500 neologisms, as well the bot can give advice on jargonisms and other types of specific words. Also in 2021, Nanosemanics Lab has signed the first-ever Russian "Codex of ethics of artificial intelligence". It establishes guidelines for ethical behavior of businesses that implement AI-based solutions. === IT contests === The company regularly organizes All-Russian Turing Test competitions for IT developers. Some of these events are co-organized with Microsoft. During the competitions, judges randomly choose virtual interlocutor and have a short conversation with them. They have to determine if a human or a machine is talking to them. An interlocutor may be either a bot or its human creator or operator. The results are measured in per cent of judges that were successfully convinced by a machine that it was a human. In 2021 Nanosemantics took part in federal project "Artificial Intelligence" by National Technological Initiative. In December 2021 the company together with state enterprise "Resource Center of Universal Design and Rehabilitation Technologies" (RCUD-RT) held an all-Russian hackathon aimed at development of AI solutions for medicine. During 3 days, participants created several training programs for patients with speech disorders. In April 2022, another hackathon by Nanosemantics was held together with MIREA – Russian Technological University. Students were participating and trying to generate algorithms for voice deepfakes. 17 teams contested in creation of software that generated artificial voice of a certain person. == Recognition == Since its foundation, Nanosemantics Lab has received a number of recognitions and awards. Among them are several professional ROTOR awards for the website iii.ru (created in 2009). The website gives the general public the means to create and train virtual assistants, which can then be used on a website or integrated into social networks. In 2013, a virtual assistant called Dana, created for Beeline Kazakhstan, was awarded with professional prize "Crystal Headset" in nomination "the best applying of technology". In 2015, the RBTH international media service included Nanosemantics in its list of "Top 50 Startups" in Russia. In 2016, the company received Russian state-maintained award called Runet Prize in two nominations: "State and Society" and "Technology and Innovation". In 2021, in Velikiy Novgorod, Nanosemantics team has won a hackathon aimed at finding means of discovering corruption schemes in Russian laws. In February 2022 the company won another contest by National Technological Initiative, called "Prochtenie", aimed at creation of AI systems for checking schoolchildren's school essays. The Nanosemantics team was awarded 20 million rubles for "overcoming technological barrier" in contest dedicated to English language, and 12 million for 1st place in special nomination "Structure" in Russian-language essay contest.
Pyramid (image processing)
Pyramid, or pyramid representation, is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling. Pyramid representation is a predecessor to scale-space representation and multiresolution analysis. == Pyramid generation == There are two main types of pyramids: lowpass and bandpass. A lowpass pyramid is made by smoothing the image with an appropriate smoothing filter and then subsampling the smoothed image, usually by a factor of 2 along each coordinate direction. The resulting image is then subjected to the same procedure, and the cycle is repeated multiple times. Each cycle of this process results in a smaller image with increased smoothing, but with decreased spatial sampling density (that is, decreased image resolution). If illustrated graphically, the entire multi-scale representation will look like a pyramid, with the original image on the bottom and each cycle's resulting smaller image stacked one atop the other. A bandpass pyramid is made by forming the difference between images at adjacent levels in the pyramid and performing image interpolation between adjacent levels of resolution, to enable computation of pixelwise differences. == Pyramid generation kernels == A variety of different smoothing kernels have been proposed for generating pyramids. Among the suggestions that have been given, the binomial kernels arising from the binomial coefficients stand out as a particularly useful and theoretically well-founded class. Thus, given a two-dimensional image, we may apply the (normalized) binomial filter (1/4, 1/2, 1/4) typically twice or more along each spatial dimension and then subsample the image by a factor of two. This operation may then proceed as many times as desired, leading to a compact and efficient multi-scale representation. If motivated by specific requirements, intermediate scale levels may also be generated where the subsampling stage is sometimes left out, leading to an oversampled or hybrid pyramid. With the increasing computational efficiency of CPUs available today, it is in some situations also feasible to use wider supported Gaussian filters as smoothing kernels in the pyramid generation steps. === Gaussian pyramid === In a Gaussian pyramid, subsequent images are weighted down using a Gaussian average (Gaussian blur) and scaled down. Each pixel containing a local average corresponds to a neighborhood pixel on a lower level of the pyramid. This technique is used especially in texture synthesis. === Laplacian pyramid === A Laplacian pyramid is very similar to a Gaussian pyramid but saves the difference image of the blurred versions between each levels. Only the smallest level is not a difference image to enable reconstruction of the high resolution image using the difference images on higher levels. This technique can be used in image compression. === Steerable pyramid === A steerable pyramid, developed by Simoncelli and others, is an implementation of a multi-scale, multi-orientation band-pass filter bank used for applications including image compression, texture synthesis, and object recognition. It can be thought of as an orientation selective version of a Laplacian pyramid, in which a bank of steerable filters are used at each level of the pyramid instead of a single Laplacian or Gaussian filter. == Applications of pyramids == === Alternative representation === In the early days of computer vision, pyramids were used as the main type of multi-scale representation for computing multi-scale image features from real-world image data. More recent techniques include scale-space representation, which has been popular among some researchers due to its theoretical foundation, the ability to decouple the subsampling stage from the multi-scale representation, the more powerful tools for theoretical analysis as well as the ability to compute a representation at any desired scale, thus avoiding the algorithmic problems of relating image representations at different resolution. Nevertheless, pyramids are still frequently used for expressing computationally efficient approximations to scale-space representation. === Detail manipulation === Levels of a Laplacian pyramid can be added to or removed from the original image to amplify or reduce detail at different scales. However, detail manipulation of this form is known to produce halo artifacts in many cases, leading to the development of alternatives such as the bilateral filter. Some image compression file formats use the Adam7 algorithm or some other interlacing technique. These can be seen as a kind of image pyramid. Because those file format store the "large-scale" features first, and fine-grain details later in the file, a particular viewer displaying a small "thumbnail" or on a small screen can quickly download just enough of the image to display it in the available pixels—so one file can support many viewer resolutions, rather than having to store or generate a different file for each resolution.
Information schema
In relational databases, the information schema (information_schema) is an ANSI-standard set of read-only views that provide information about all of the tables, views, columns, and procedures in a database. It can be used as a source of the information that some databases make available through non-standard commands, such as: the SHOW command of MySQL the DESCRIBE command of Oracle's SQLPlus the \d command in psql (PostgreSQL's default command-line program). => SELECT count(table_name) FROM information_schema.tables; count ------- 99 (1 row) => SELECT column_name, data_type, column_default, is_nullable FROM information_schema.columns WHERE table_name='alpha'; column_name | data_type | column_default | is_nullable -------------+-----------+----------------+------------- foo | integer | | YES bar | character | | YES (2 rows) => SELECT FROM information_schema.information_schema_catalog_name; catalog_name -------------- johnd (1 row) == Implementation == As a notable exception among major database systems, Oracle does not as of 2015 implement the information schema. An open-source project exists to address this. RDBMSs that support information_schema include: Amazon Redshift Apache Hive Microsoft SQL Server MonetDB Snowflake MySQL PostgreSQL H2 Database HSQLDB InterSystems Caché MariaDB SingleStore (formerly MemSQL) Mimer SQL Snowflake Trino Presto CrateDB ClickHouse CockroachDB Kinetica DB TiDB RDBMSs that do not support information_schema include: Apache Derby Apache Ignite Firebird Microsoft Access IBM Informix Ingres IBM Db2 Oracle Database SAP HANA SQLite Sybase ASE Sybase SQL Anywhere Teradata Vertica
Open Syllabus Project
The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. Founded by researchers from the American Assembly at Columbia University, the OSP has amassed the most extensive collection of searchable syllabi. Since its beta launch in 2016, the OSP has collected over 7 million course syllabi from over 80 countries, primarily by scraping publicly accessible university websites. The project is directed by Joe Karaganis. == History == The OSP was formed by a group of data scientists, sociologists, and digital-humanities researchers at the American Assembly, a public-policy institute based at Columbia University. The OSP was partly funded by the Sloan Foundation and the Arcadia Fund. Joe Karaganis, former vice-president of the American Assembly, serves as the project director of the OSP. The project builds on prior attempts to archive syllabi, such as H-Net, MIT OpenCourseWare, and historian Dan Cohen's defunct Syllabus Finder website (Cohen now sits on the OSP's advisory board). The OSP became a non-profit and independent of the American Assembly in November 2019. In January 2016, the OSP launched a beta version of their "Syllabus Explorer," which they had collected data for since 2013. The Syllabus Explorer allows users to browse and search texts from over one million college course syllabi. The OSP launched a more comprehensive version 2.0 of the Syllabus Explorer in July 2019. The newer version includes an interactive visualization that displays texts as dots on a knowledge map. As of 2022, the OSP has collected over 7 million course syllabi. The Syllabus Explorer represents the "largest collection of searchable syllabi ever amassed." == Methodology == The OSP has collected syllabi data from over 80 countries dating to 2000. The syllabi stem from over 4,000 worldwide institutions. Most of the OSP's data originates from the United States. Canada, Australia, and the U.K also have large datasets. The OSP primarily collects syllabi by scraping publicly accessible university websites. The OSP also allows syllabi submissions from faculty, students, and administrators. The OSP developers use machine learning and natural language processing to extract metadata from such syllabi. Since only metadata is collected, no individual syllabus or personal identifying information is found in the OSP database. The OSP classifies the syllabi into 62 subject fields – corresponding to the U.S. Department of Education's Classification of Instructional Programs (CIP). Additionally, the OSP assigns each text a "teaching score" from 0–100. This score represents the text's percentile rank among citations in the total citation count and is a numerical indicator of the relative frequency of which a particular work is taught. The OSP also has data on which texts are most likely to be assigned together. The developers behind the OSP admit that the database is incomplete and likely contains "a fair number of errors." Karaganis estimates that 80–100 million syllabi exist in the United States alone. The OSP is unable to access syllabi behind private course-management software like Blackboard. == Notable findings == === Anthropology === Using data from the OSP, anthropologist Laurence Ralph uncovered that black anthropologists are "woefully under-represented in (if not erased from) most anthropology syllabi." Black authors wrote less than 1 percent of the top 1,000 assigned works. === Economics === The database indicates Greg Mankiw is the most frequently cited author for college economics courses. === English literature === The OSP found that Mary Shelley's Frankenstein was the most widely taught novel in college courses. Additionally, the majority of novels published after 1945 taught in English classes were historical fiction. === Female writers === The most read female writer on college campuses is Kate L. Turabian for her A Manual for Writers of Research Papers, Theses, and Dissertations . Turabian is followed by Diana Hacker, Toni Morrison, Jane Austen, and Virginia Woolf. === Film === The most assigned film according to the OSP is the 1929 Soviet documentary film, Man with a Movie Camera. English filmmaker Alfred Hitchcock is the most assigned director in college courses. === History === Historians George Brown Tindall and David Emory Shi's America: A Narrative History is the number one assigned textbook for history, followed by Anne Moody's memoir, Coming of Age in Mississippi. === Philosophy === The most assigned texts in the field of philosophy include Aristotle's Nicomachean Ethics, John Stuart Mill's Utilitarianism, and Plato's Republic. Plato's Republic was also the second most assigned text in universities in the English-speaking world (only behind Strunk and White's Elements of Style). === Physics === David Halliday's et al. Fundamentals of Physics is the number one ranked physics textbook in the OSP's database. === Political science === Data from the OSP indicates that the dominant political science texts are written almost exclusively by white men and scholars based in the West. In the top 200 most-frequently assigned works, 15 are authored by at least one woman. === Public administration === American president Woodrow Wilson's article "The Study of Administration" was the most frequently assigned text in public affairs and administration syllabi. == Reception == According to William Germano et al., the OSP is a "fascinating resource but is also prone to misrepresenting or at least distracting us from the most important business of a syllabus: communicating with students." Historian William Caferro remarks that the OSP is a "tacit experience of sharing, but a useful one." English professor Bart Beaty writes that, "Despite the many reservations about the completeness of its data, the OSP provides a rare opportunity for scholars to move beyond the anecdotal in discussions of canon-formation in teaching." Media theorist Elizabeth Losh opines that "big data approaches", like the OSP, may "raise troubling questions for instructors about informed consent, pedagogical privacy, and quantified metrics."