AI Analysis Youtube Video

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  The Triple Revolution

  "The Triple Revolution" was an open memorandum sent to U.S. President Lyndon B. Johnson and other government figures on March 22, 1964. It concerned three megatrends of the time: increasing use of automation, the nuclear arms race, and advancements in human rights. Drafted under the auspices of the Center for the Study of Democratic Institutions, it was signed by an array of noted social activists, professors, and technologists who identified themselves as the Ad Hoc Committee on the Triple Revolution. The chief initiator of the proposal was W. H. "Ping" Ferry, at that time a vice-president of CSDI, basing it in large part on the ideas of the futurist Robert Theobald. == Overview == The statement identified three revolutions underway in the world: the cybernation revolution of increasing automation; the weaponry revolution of mutually assured destruction; and the human rights revolution. It discussed primarily the cybernation revolution. The committee claimed that machines would usher in "a system of almost unlimited productive capacity" while continually reducing the number of manual laborers needed, and increasing the skill needed to work, thereby producing increasing levels of unemployment. It proposed that the government should ease this transformation through large-scale public works, low-cost housing, public transit, electrical power development, income redistribution, union representation for the unemployed, and government restraint on technology deployment. == Legacy == Martin Luther King Jr.'s final Sunday sermon, delivered six days before his April 1968 assassination, explicitly references the thesis of "The Triple Revolution": There can be no gainsaying of the fact that a great revolution is taking place in the world today. In a sense it is a triple revolution: that is, a technological revolution, with the impact of automation and cybernation; then there is a revolution in weaponry, with the emergence of atomic and nuclear weapons of warfare; then there is a human rights revolution, with the freedom explosion that is taking place all over the world. Yes, we do live in a period where changes are taking place. And there is still the voice crying through the vista of time saying, "Behold, I make all things new; former things are passed away." In Harlan Ellison's 1967 anthology Dangerous Visions, Philip José Farmer's story "Riders of the Purple Wage" uses the Triple Revolution document as the premise of a future society, in which the "purple wage" of the title is a guaranteed income dole on which most of the population lives. At the 1968 World Science Fiction Convention in San Francisco, Farmer delivered a lengthy Guest of Honor speech in which he called for the founding of a grassroots activist organization called REAP which would work for implementation of the Ad Hoc Committee's recommendations. Looking back on the proposal in his 2008 book, Daniel Bell wrote: "the cybernetic revolution quickly proved to be illusory. There were no spectacular jumps in productivity. ... Cybernation had proved to be one more instance of the penchant for overdramatizing a momentary innovation and blowing it up far out of proportion to its actuality. ... The image of a completely automated production economy—with an endless capacity to turn out goods—was simply a social-science fiction of the early 1960s. Paradoxically, the vision of Utopia was suddenly replaced by the spectre of Doomsday. In place of the early-sixties theme of endless plenty, the picture by the end of the decade was one of a fragile planet of limited resources whose finite stocks were being rapidly depleted, and whose wastes from soaring industrial production were polluting the air and waters." In his 2015 book Rise of the Robots, Martin Ford claims The Triple Revolution's predictions of steady decline in future employment were not wrong, but rather premature. He cites "Seven Deadly Trends" that began in the 1970s-1980s and by the mid-2010s appeared set to continue: Stagnation in real wages Decline in labor's share of national income in many countries (breakdown of Bowley's law), while corporate profits increased Declining labor force participation Diminishing job creation, lengthening jobless recoveries, and soaring long-term unemployment Rising inequality Declining incomes, and underemployment for recent college graduates Polarization and part-time jobs (middle-class jobs are disappearing, to be replaced by a small number of high-paying jobs and large number of low-paying jobs) According to Ford, the 1960s were part of what in retrospect seems like a golden age for labor in the United States, when productivity and wages rose together in near lockstep, and unemployment was low. But after about 1980, wages began stagnating while productivity continued to rise. Labor's share of the economic output began to decline. Ford describes the role that automation and information technology play in these trends, and how new technologies including narrow AI threaten to destroy jobs faster than displaced workers can be retrained for new jobs, before automation takes the new jobs as well. This includes many job categories, such as in transportation, that were never threatened by automation before. According to a 2013 study, about 47% of US jobs are susceptible to automation. == Signatories ==

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  Ware report

  Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.

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  BeyondCorp

  BeyondCorp is an implementation of zero-trust computer security concepts creating a zero trust network. It is created by Google. == Background == It was created in response to the 2009 Operation Aurora. An open source implementation inspired by Google's research paper on an access proxy is known as "transcend". Google documented its Zero Trust journey from 2014 to 2018 through a series of articles in the journal ;login:. Google called their ZT network "BeyondCorp". Google implemented a Zero Trust architecture on a large scale, and relied on user and device credentials, regardless of location. Data was encrypted and protected from managed devices. Unmanaged devices, such as BYOD, were not given access to the BeyondCorp resources. == Design and technology == BeyondCorp utilized a zero trust security model, which is a relatively new security model that it assumes that all devices and users are potentially compromised. This is in contrast to traditional security models, which rely on firewalls and other perimeter defenses to protect sensitive data. === Trust === The corporate network grants no inherent trust, and all internal apps are accessed via the BeyondCorp system, regardless of whether the user is in a Google office or working remotely. BeyondCorp is related to Zero Trust architecture as it implements a true Zero Trust network, where all access is granted on identity, device, and authentication, based on robust underlying device and identity data sources. BeyondCorp works by using a number of security policies including authentication, authorization, and access control to ensure that only authorized users can access corporate resources. Authentication verifies the identity of the user, authorization determines whether the user has permission to access the requested resource, and access control policies restrict what the user can do with the resource. ==== Trust Inferrer ==== One of the main components in BeyondCorp's implementation is the Trust Inferrer. The Trust Inferrer is a security component (typically software) that looks at information about a user's device, like a computer or phone, to decide how much it can be trusted to access certain resources like important company documents. The Trust Inferrer checks things like the security of the device, whether it has the right software installed, and if it belongs to an authorized user. Based on all this information, the Trust Inferrer decides what the device can access and what it can't. === Security mechanisms === Unlike traditional VPNs, BeyondCorp's access policies are based on information about a device, its state, and its associated user. BeyondCorp considers both internal networks and external networks to be completely untrusted, and gates access to applications by dynamically asserting and enforcing levels, or “tiers,” of access. === Device Inventory Database === BeyondCorp utilized a Device Inventory Database and Device Identity that uniquely identifies a device through a digital certificate. Any changes to the device are recorded in the Device Inventory Database. The certificate is used to uniquely identify a device; however, additional information is required to grant access privileges to a resource. === Access Control Engine === Another important component of BeyondCorp's implementation is the Access Control Engine. Think of this as the brain of the Zero Trust architecture. The Access Control Engine is like a traffic cop standing at an intersection. Its job is to make sure that only authorized devices and users are allowed to access specific resources (like files or applications) on the network. It checks the access policy (the rules that say who can access what), the device's state (like whether it has the right software updates or security settings), and the resources being requested. Then it makes a decision on whether to grant or deny access based on all of this information. It helps ensure that only the right people and devices are allowed access to the network, which helps keep things secure. The Access Control Engine utilizes the output from the Trust Inferrer and other data that is fed into its system. == Usage == One of the first things Google did to implement a Zero Trust architecture was to capture and analyze network traffic. The purpose of analyzing the traffic was to build a baseline of what typical network traffic looked like. In doing so, BeyondCorp also discovered unusual, unexpected, and unauthorized traffic. This was very useful because it gave the BeyondCorp engineers critical information that assisted them in reengineering the system in a secure manner. Some of the benefits BeyondCorp realized by adopting a Zero Trust architecture include the ability to allow their employees to work securely from any location. It reduces the risk of data breaches since data and applications are protected and users and devices are constantly being verified. The Zero Trust architecture is scalable and can be adapted to the changing needs of the businesses and their users. Especially relevant in today's work-from-home era, BeyondCorp allows employees to access enterprise resources securely from any location, without the need for traditional VPNs.

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  INaturalist

  iNaturalist is an American 501(c)(3) nonprofit social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe. iNaturalist may be accessed via its website or from its mobile applications. iNaturalist includes an automated species identification tool, and users further assist each other in identifying organisms from photographs and sound recordings. As of 5 August 2025, iNaturalist users had contributed nearly 300 million observations of plants, animals, fungi, and other organisms worldwide, and 400,000 users were active in the previous 30 days. iNaturalist serves as an important resource of open data for biodiversity research, conservation, and education, describing itself as "an online social network of people sharing biodiversity information to help each other learn about nature." It is the primary application for crowd-sourced biodiversity data in places such as Mexico, southern Africa, and Australia, and the project has been called "a standard-bearer for natural history mobile applications." Most of iNaturalist's software is open source. It has contributed to over 4,000 research papers and is widely used by scientists, land managers, and conservationists worldwide. The platform has also been active in the discovery of new species and rediscovery of species previously assumed to be extinct. == History == iNaturalist began in 2008 as a UC Berkeley School of Information Master's final project of Nate Agrin, Jessica Kline, and Ken-ichi Ueda. Agrin and Ueda continued work on the site with Sean McGregor, a web developer. In 2011, Ueda began collaboration with Scott Loarie, a research fellow at Stanford University and lecturer at UC Berkeley. Ueda and Loarie are the current co-directors of iNaturalist.org. The organization merged with the California Academy of Sciences on 24 April 2014. In 2017, iNaturalist became a joint initiative between the California Academy of Sciences and the National Geographic Society. With these collaborations and growing popularity of the site since 2012, the number of participants and observations has roughly doubled each year. In 2014, iNaturalist reached 1 million observations. Later, as of October 2023, there were 181 million observations (163 million verifiable). On 11 July 2023 iNaturalist announced its status as a newly independent 501(c)(3) nonprofit organization. === Google AI controversy === On 9 June 2025 Google announced that iNaturalist would be part of its "Generative AI Accelerator". This announcement, paired with the initial lack of information on the iNaturalist site, led to outcry from many iNaturalist users in the blog comments and forum, worrying about the consequences for the environment, volunteer engagement, reliability and raised questions about the decision making within iNaturalist, while some saw the backlash as a sign that people want to resist 'corrosive technologies'. PZ Myers, a biology professor who uses iNaturalist in his teaching, published an article on his website Pharyngula stating that "any decision that drives people away and replaces them with a hallucinating bot is a bad decision". == Platforms == Users can interact with iNaturalist in the following ways: through the iNaturalist.org website, through two mobile apps: iNaturalist (iOS/Android) and Seek by iNaturalist (iOS/Android), or through partner organizations such as the Global Biodiversity Information Facility (GBIF) website. On the iNaturalist.org website, visitors can search the public dataset and interact with other people adding observations and identifications. The website provides tools for registered users to add, identify, and discuss observations, write journal posts, explore information about species, create project pages to recruit participation, and coordinate work on their topics of interest. On the iNaturalist mobile app, users can create and share nature observations to the online dataset, explore observations both nearby and around the world, and learn about different species. Seek by iNaturalist, a separate app marketed to families, requires no online account registration and all observations may remain private. Seek incorporates features of gamification, such as providing a list of nearby organisms to find and encouraging the collection of badges and participation in challenges. Seek was initially released in the spring of 2018. == Observations == The iNaturalist platform is based on crowdsourcing of observations and identifications. An iNaturalist observation records a person's encounter with an individual organism at a particular time and place. An iNaturalist observation may also record evidence of an organism, such as animal tracks, nests, or scat. The scope of iNaturalist excludes natural but inert subjects such as geologic or hydrologic features. Users typically upload photos as evidence of their findings, though audio recordings are also accepted, and such evidence is not a strict requirement. Users may share observation locations publicly, "obscure" them to display a less precise location or make the locations completely private. iNaturalist users can add identifications to each other's observations in order to confirm or improve the identification of the observation. Observations are classified as "Casual", "Needs ID" (needs identification), or "Research Grade" based on the quality of the data provided and the community identification process. Any quality of data can be downloaded from iNaturalist and "Research Grade" observations are often incorporated into other online databases such as the Global Biodiversity Information Facility and the Atlas of Living Australia. === Automated species identification === In addition to observations being identified by others in the community, iNaturalist includes an automated species identification tool, first released in 2017. Images can be identified via a computer vision model which has been trained on the large database of the observations on iNaturalist. Multiple species suggestions are typically provided with the suggestion that the software guesses to be most likely is at the top of the list. A broader taxon such as a genus or family is commonly provided if the model is unsure of the species. It is trained once or twice a year, and the threshold for species included in the training set has changed over time. It can be difficult for the model to guess correctly if the species in question is infrequently observed or hard to identify from images alone, or if the image submitted has poor lighting, is blurry, or contains multiple subjects. In February 2023, iNaturalist released v2.1 of its computer vision model, which was trained on a new source model which performed significantly better than the previous models trained using a different source model. In April 2025 iNaturalist released an updated app for iOS, changing the original version to "iNaturalist Classic." == Projects == Users have created and contributed to tens of thousands of different projects on iNaturalist. The platform is commonly used to record observations during bioblitzes, which are biological surveying events that attempt to record all the species that occur within a designated area, and a specific project type on iNaturalist. Other project types include collections of observations by location or taxon or documenting specific types of observations such as animal tracks and signs, the spread of invasive species, roadkill, fishing catches, or discovering new species. In 2011, iNaturalist was used as a platform to power the Global Amphibian and Global Reptile BioBlitzes, in which observations were used to help monitor the occurrence and distribution of the world's reptiles and amphibian species. The US National Park Service partnered with iNaturalist to record observations from the 2016 National Parks BioBlitz. That project exceeded 100,000 observations in August 2016. In 2017, the United Nations Environment Programme teamed up with iNaturalist to celebrate World Environment Day.. In 2022, Reef Ecologic teamed up with iNaturalist to celebrate World Oceans Day. === City Nature Challenge === In 2016, Lila Higgins from the Natural History Museum of Los Angeles County and Alison Young from the California Academy of Sciences co-founded the City Nature Challenge (CNC). In the first City Nature Challenge, naturalists in Los Angeles and the San Francisco Bay Area documented over 20,000 observations with the iNaturalist platform. In 2017, the CNC expanded to 16 cities across the United States and collected over 125,000 observations of wildlife in 5 days. The CNC expanded to a global audience in 2018, with 68 cities participating from 19 countries, with some cities using community science platforms other than iNaturalist to participate. In 4 days, over 17,000 people cataloged over 440,000 nature observations in urban regions around the world. In 2019, the CNC once again expanded, with 35,000 parti

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  FlowVella

  FlowVella (formerly Flowboard) is an interactive presentation platform that includes an iPad/iPhone app, a Mac app and web site for viewing presentations, built first for the iPad and web. FlowVella allows users to create, publish and share presentations through their cloud-based SaaS system. FlowVella allows embedding of text, images, PDFs, video and gallery objects in easy linkable screens, defining modern interactive presentations. FlowVella grew out of Treemo Labs. == History == FlowVella launched as 'Flowboard' on April 18, 2013 after being built for almost a year. FlowVella was incubated out of Treemo Labs, which had years of experience building native apps for iPhone, iPad and Android devices. FlowVella is an iPad app and Mac app where users create, view, publish and share interactive presentations. Presentations are viewable on flowvella.com through a web-based viewer on any device or through the FlowVella native iPad app or Mac app. On December 18, 2014, Flowboard rebranded as FlowVella after a trademark dispute. == Presentation format == FlowVella is an interactive presentation format where instead of single directional slides, presentations are made up of linkable screens with embeddable media and content objects. While 'Flows' can be exported to PDF, they all have a web address and are meant to be viewed via a web browser or the FlowVella native applications. == Revenue model == FlowVella uses the freemium model for its presentation apps. Free users can make 4 public presentations with limited number of screens/slides, but most features are available to try out the software. In 2016, FlowVella introduced a second paid plan called PRO which includes team sharing, tracking and newly introduced 'Kiosk Mode' that launched in March of 2017. == Features == FlowVella is a native iPad app and Mac app which has advantages over web based tools. All downloaded presentations can be viewed offline, without an Internet connection. This includes videos which are enabled by caching the video files into memory. For students, teachers, sales people and all users, this is extremely important because this prevents having a presentation fail because of lack of an Internet connection. Beyond the offline capabilities, there is a trend to build native applications versus HTML5 as noted by Facebook and LinkedIn both rebuilding their mobile apps as 100% native applications.

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  Sherwood Applied Business Security Architecture

  SABSA (Sherwood Applied Business Security Architecture) is a model and methodology for developing a risk-driven enterprise information security architecture and service management, to support critical business processes. It was developed independently from the Zachman Framework, but has a similar structure. The primary characteristic of the SABSA model is that everything must be derived from an analysis of the business requirements for security, especially those in which security has an enabling function through which new business opportunities can be developed and exploited. The process analyzes the business requirements at the outset, and creates a chain of traceability through the strategy and concept, design, implementation, and ongoing ‘manage and measure’ phases of the lifecycle to ensure that the business mandate is preserved. Framework tools created from practical experience further support the whole methodology. The model is layered, with the top layer being the business requirements definition stage. At each lower layer a new level of abstraction and detail is developed, going through the definition of the conceptual architecture, logical services architecture, physical infrastructure architecture and finally at the lowest layer, the selection of technologies and products (component architecture). The SABSA model itself is generic and can be the starting point for any organization, but by going through the process of analysis and decision-making implied by its structure, it becomes specific to the enterprise, and is finally highly customized to a unique business model. It becomes in reality the enterprise security architecture, and it is central to the success of a strategic program of information security management within the organization. SABSA is a particular example of a methodology that can be used both for IT (information technology) and OT (operational technology) environments. == SABSA matrix == Note: The above is the original SABSA Matrix, which is still valid today, but it has been expanded by a comprehensive service management matrix and updated in some detail and terminology areas. In the words of David Lynas, SABSA author, "The SABSA Matrix and the SABSA Service Management Matrix have not been updated since the late 90s. We have redesigned them to deliver the improvements your feedback has requested over the years. We have not fundamentally changed the structure or principles of the matrices (very few elements have changed position) but have focused on terminology update and consistency." The new versions can be downloaded (along with the 2009 revision of the SABSA White Paper and other important documents like the SABSA Certification Roadmap) at the SABSA Members' Web Site.

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  Simple interactive object extraction

  Simple interactive object extraction (SIOX) is an algorithm for extracting foreground objects from color images and videos with very little user interaction. It has been implemented as "foreground selection" tool in the GIMP (since version 2.3.3), as part of the tracer tool in Inkscape (since 0.44pre3), and as function in ImageJ and Fiji (plug-in). Experimental implementations were also reported for Blender and Krita. Although the algorithm was originally designed for videos, virtually all implementations use SIOX primarily for still image segmentation. In fact, it is often said to be the current de facto standard for this task in the open-source world. Initially, a free hand selection tool is used to specify the region of interest. It must contain all foreground objects to extract and as few background as possible. The pixels outside the region of interest form the sure background while the inner region define a superset of the foreground, i.e. the unknown region. A so-called foreground brush is then used to mark representative foreground regions. The algorithm outputs a selection mask. The selection can be refined by either adding further foreground markings or by adding background markings using the background brush. Technically, the algorithm performs the following steps: Create a set of representative colors for sure foreground and sure background, the so-called color signatures. Assign all image points to foreground or background by a weighted nearest neighbor search in the color signatures. Apply some standard image processing operations like erode, dilate, and blur to remove artifacts. Find the connected foreground components that are either large enough or marked by the user. For video segmentation the sure background and sure foreground regions are learned from motion statistics. SIOX also features tools that allow sub-pixel accurate refinement of edges and high texture areas, the so-called "detail refinement brushes". As with all segmentation algorithms, there are always pictures where the algorithm does not yield perfect results. The most critical drawback of SIOX is the color dependence. Although many photos are well-separable by color, the algorithm cannot deal with camouflage. If the foreground and background share many identical shades of similar colors, the algorithm might give a result with parts missing or incorrectly classified foreground. SIOX performs about equally well on different benchmarks compared to graph-based segmentation methods, such as Grabcut. SIOX is, however, more noise robust and can therefore also be used for the segmentation of videos. Graph-based segmentation methods search for a minimum cut and therefore tend to not perform optimally with complex structures. The algorithm has initially been developed at the department of computer science at Freie Universitaet Berlin. The main developer, Gerald Friedland, is now faculty at the EECS department of the University of California at Berkeley and also a Principal Data Scientist at Lawrence Livermore National Lab. He continues to support the development through mentoring, e.g. in the Google Summer of Code.

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  Lost Art-Database

  The Lost Art-Datenbank is an online database published by the German Lost Art Foundation (Deutsches Zentrum Kulturgutverluste. It contains information on cultural objects looted from Jewish collectors or transferred due to Nazi persecution during the Nazi era. Until 2015, it was managed by the Koordinierungsstelle für Kulturgutverluste (Magdeburg Coordination Office). == Creation == Following the Washington Conference of 1998, and the commitments to provide more transparency regarding looted art, Germany launched the Lost Art Database in 2000 order to help Holocaust victims and their families track down artworks that had been looted from them or lost due to Nazi persecution. == Functionality == The Lost Art Database lists art and books and other cultural objects that were lost, seized, stolen or forceably sold during the Nazi era. The database is divided into search requests from victims' families, heirs or institutions and "found" reports from cultural institutions on items with unresolved provenance gaps from the Nazi periods. The section on reports of finds lists objects that are known to have been unlawfully seized or relocated as a result of the war. In addition, reports are published here on cultural objects for which an uncertain or incomplete provenance may indicate a possible unlawful seizure or war-related relocation. The publication of reports in the Lost Art Internet Database is carried out on behalf of and with the consent of the reporting persons and institutions. The responsibility for the content of the reports lies with these legal or natural persons. There have been controversies over which items should be included in the database. Lost Art is based on the Washington Principles adopted in 1998, which Germany has committed itself to implementing (Joint Declaration, 1999). The Lost Art Database is considered a key resource in the search for looted art and the victims of persecution. Every item in the Lost Art Database has an identifier, known as a Lost Art ID. Proveana is the linked research database. == Other lost art databases == Other countries have launched databases to help identify Nazi looted art. Each database has its own area of focus. The German Lost Art Database allows families or heirs to submit information. Other countries have databases that focus on looted artworks that have not been found or artworks that were repatriated to the national authorities after the defeat of the Nazis but were never returned to their original owners. Other databases have been created for stolen antiquities, looted art from colonial era, art stolen from Syria, Iraq, Ukraine, or from museums or collectors.

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  Soterml

  SoTerML (Soil and Terrain Markup Language) is a XML-based markup language for storing and exchanging soil and terrain related data. SoTerML development is being done within The e-SoTer Platform. GEOSS plans a global Earth Observation System and, within this framework, the e-SOTER project addresses the felt need for a global soil and terrain database. The Centre for Geospatial Science (Currently Nottingham Geospatial Institute) at the University of Nottingham has initiated the development since January 2009. Further development and maintenance is currently handled in National Soil Resources Institute (NSRI) at Cranfield University, UK. The role of CGS is within the development of the e-SOTER dissemination platform, which is based on INSPIRE principles. The SoTerML development included: 1. Development of a data dictionary for nomenclatures and various data sources (data and metadata). 2. Development of an exchange format/procedures from the World Reference Base 2006.

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  Stencil buffer

  A stencil buffer is an extra data buffer, in addition to the color buffer and Z-buffer, found on modern graphics hardware. The buffer is per pixel and works on integer values, usually with a depth of one byte per pixel. The Z-buffer and stencil buffer often share the same area in the RAM of the graphics hardware. In the simplest case, the stencil buffer is used to limit the area of rendering (stenciling). More advanced usage of the stencil buffer makes use of the strong connection between the Z-buffer and the stencil buffer in the rendering pipeline. For example, stencil values can be automatically increased/decreased for every pixel that fails or passes the depth test. The simple combination of depth test and stencil modifiers make a vast number of effects possible (such as stencil shadow volumes, Two-Sided Stencil, compositing, decaling, dissolves, fades, swipes, silhouettes, outline drawing, or highlighting of intersections between complex primitives) though they often require several rendering passes and, therefore, can put a heavy load on the graphics hardware. The most typical application is still to add shadows to 3D applications. It is also used for planar reflections. Other rendering techniques, such as portal rendering, use the stencil buffer in other ways; for example, it can be used to find the area of the screen obscured by a portal and re-render those pixels correctly. The stencil buffer and its modifiers can be accessed in computer graphics by using APIs like OpenGL, Direct3D, Vulkan or Metal. == Architecture == The stencil buffer typically shares the same memory space as the Z-buffer, and typically the ratio is 24 bits for Z-buffer + 8 bits for stencil buffer or, in the past, 15 bits for Z-buffer + 1 bit for stencil buffer. Another variant is 4 + 24, where 28 of the 32 bits are used and 4 ignored. Stencil and Z-buffers are part of the frame buffer, coupled to the color buffer. The first chip available to a wider market was 3Dlabs' Permedia II, which supported a one-bit stencil buffer. The bits allocated to the stencil buffer can be used to represent numerical values in the range [0, 2n-1], and also as a Boolean matrix (n is the number of allocated bits), each of which may be used to control the particular part of the scene. Any combination of these two ways of using the available memory is also possible. == Stencil test == Stencil test or stenciling is among the operations on the pixels/fragments (Per-pixel operations), located after the alpha test, and before the depth test. The stencil test ensures undesired pixels do not reach the depth test. This saves processing time for the scene. Similarly, the alpha test can prevent corresponding pixels to reach the stencil test. The test itself is carried out over the stencil buffer to some value in it, or altered or used it, and carried out through the so-called stencil function and stencil operations. The stencil function is a function by which the stencil value of a certain pixel is compared to a given reference value. If this comparison is logically true, the stencil test passes. Otherwise not. In doing so, the possible reaction caused by the result of comparing three different state-depth and stencil buffer: Stencil test is not passed Stencil test is passed but not the depth test Both tests are passed (or stencil test is passed, and the depth is not enabled) For each of these cases, different operations can be set over the examined pixel. In the OpenGL stencil functions, the reference value and mask, respectively, define the function glStencilFunc. In Direct3D each of these components is adjusted individually using methods SetRenderState devices currently in control. This method expects two parameters, the first of which is a condition that is set and the other its value. In the order that was used above, these conditions are called D3DRS_STENCILFUNC, D3DRS_STENCILREF, and D3DRS_STENCILMASK. Stencil operations in OpenGL adjust glStencilOp function that expects three values. In Direct3D, again, each state sets a specific method SetRenderState. The three states that can be assigned to surgery are called D3DRS_STENCILFAIL, D3DRENDERSTATE_STENCILZFAIL, and D3DRENDERSTATE_STENCILPASS. == Z-fighting == Due to the lack of precision in the Z-buffer, coplanar polygons that are short-range, or overlapping, can be portrayed as a single plane with a multitude of irregular cross-sections. These sections can vary depending on the camera position and other parameters and are rapidly changing. This is called Z-fighting. There exist multiple solutions to this issue: - Bring the far plane closer to restrict the scene's depth, thus increasing the accuracy of the Z-buffer, or reducing the distance at which objects are visible in the scene. - Increase the number of bits allocated to the Z-buffer, which is possible at the expense of memory for the stencil buffer. - Move polygons farther apart from one another, which restricts the possibilities for the artist to create an elaborate scene. All of these approaches to the problem can only reduce the likelihood that the polygons will experience Z-fighting, and do not guarantee a definitive solution in the general case. A solution that includes the stencil buffer is based on the knowledge of which polygon should be in front of the others. The silhouette of the front polygon is drawn into the stencil buffer. After that, the rest of the scene can be rendered only where the silhouette is negative, and so will not clash with the front polygon. == Shadow volume == Shadow volume is a technique used in 3D computer graphics to add shadows to a rendered scene. They were first proposed by Frank Crow in 1977 as the geometry describing the 3D shape of the region occluded from a light source. A shadow volume divides the virtual world in two: areas that are in shadow and areas that are not. The stencil buffer implementation of shadow volumes is generally considered among the most practical general-purpose real-time shadowing techniques for use on modern 3D graphics hardware. It has been popularised by the video game Doom 3, and a particular variation of the technique used in this game has become known as Carmack's Reverse. == Reflections == Reflection of a scene is drawn as the scene itself transformed and reflected relative to the "mirror" plane, which requires multiple render passes and using of stencil buffer to restrict areas where the current render pass works: Draw the scene excluding mirror areas – for each mirror lock the Z-buffer and color buffer Render visible part of the mirror Depth test is set up so that each pixel is passed to enter the maximum value and always passes for each mirror: Depth test is set so that it passes only if the distance of a pixel is less than the current (default behavior) The matrix transformation is changed to reflect the scene relative to the mirror plane Unlock the Z-buffer and color buffer Draw the scene, but only the part of it that lies between the mirror plane and the camera. In other words, a mirror plane is also a clipping plane Again locks color buffer, depth test is set so that it always passes, reset stencil for the next mirror. == Planar Shadows == While drawing a plane of shadows, there are two dominant problems: The first concerns the problem of deep struggle in case the flat geometry is not awarded on the part covered with the shadow of shadows and outside. See the section that relates to this. Another problem relates to the extent of the shadows outside the area where the plane there. Another problem, which may or may not appear, depending on the technique, the design of more polygons in one part of the shadow, resulting in darker and lighter parts of the same shade. All three problems can be solved geometrically, but because of the possibility that hardware acceleration is directly used, it is a far more elegant implementation using the stencil buffer: 1. Enable lights and the lights 2. Draw a scene without any polygon that should be projected shadows 3. Draw all polygons which should be projected shadows, but without lights. In doing so, the stencil buffer, the pixel of each polygon to be assigned to a specific value for the ground to which they belong. The distance between these values should be at least two, because for each plane to be used two values for two states: in the shadows and bright. 4. Disable any global illumination (to ensure that the next steps will affect only individual selected light) For each plane: For each light: 1. Edit a stencil buffer and only the pixels that carry a specific value for the selected level. Increase the value of all the pixels that are projected objects between the date of a given level and bright. 2. Allow only selected light for him to draw level at which part of her specific value was not changed. == Spatial shadows == Stencil buffer implementation of spatial drawing shadows is any shadow of a geometric body that its volume includes part of the scene that is

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  Elasticity (data store)

  The elasticity of a data store relates to the flexibility of its data model and clustering capabilities. The greater the number of data model changes that can be tolerated, and the more easily the clustering can be managed, the more elastic the data store is considered to be. == Types == === Clustering elasticity === Clustering elasticity is the ease of adding or removing nodes from the distributed data store. Usually, this is a difficult and delicate task to be done by an expert in a relational database system. Some NoSQL data stores, like Apache Cassandra have an easy solution, and a node can be added/removed with a few changes in the properties and by adding specifying at least one seed. === Data-modelling elasticity === Relational databases are most often very inelastic, as they have a predefined data model that can only be adapted through redesign. Most NoSQL data stores, however, do not have a fixed schema. Each row can have a different number and even different type of columns. Concerning the data store, modifications in the schema are no problem. This makes this kind of data stores more elastic concerning the data model. The drawback is that the programmer has to take into account that the data model may change over time.

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  Scenery generator

  A scenery generator (or terrain generator) is a software used to create landscape images, 3D models, and animations. These programs often use procedural generation to generate the landscapes, or sometimes created and rendered by a 3D artist. These programs are often used in video games or movies. Basic elements of landscapes created by scenery generators include terrain, water, foliage, and clouds. The process for basic random generation uses a diamond square algorithm. == Common features == Most scenery generators can create basic heightmaps to simulate the variation of elevation in basic terrain. Common techniques include Simplex noise, fractals, or the diamond-square algorithm, which can generate 2-dimensional heightmaps. A version of scenery generator can be very simplistic. Using a diamond-square algorithm with some extra steps involving fractals, an algorithm for random generation of terrain can be made with only 120 lines of code. The program in example takes a grid and then divides the grid repeatedly. Each smaller grid is then split into squares and diamonds and the algorithm then makes the randomized terrain for each square and diamond. Most programs for creating landscapes also allow for adjustment and editing of the landscape. For example, World Creator allows for terrain sculpting, which uses a similar brush system as Photoshop, and allows for additional terrain enhancement with its procedural techniques such as erosion, sediments, and more. Other tools in the World Creator program include terrain stamping, which allows you to import elevation maps and use them as a base. The programs tend to also allow for additional placement of rocks, trees, etc. These can be done procedurally or by hand depending on the program. Typically the models used for the placement objects are the same as to lessen the amount of work that would be done if the user was to create a multitude of different trees. The terrain generated the computer does a generation of multifractals then integrates them until finally rendering them onto the screen. These techniques are typically done “on-the-fly” which typically for a 128 × 128 resolution terrain would mean 1.5 seconds on a CPU from the early 1990s. == Applications == Scenery generators are commonly used in movies, animations, 3D rendering, and video games. For example, Industrial Light & Magic used E-on Vue to create the fictional environments for Pirates of the Caribbean: Dead Man's Chest. In such live-action cases, a 3D model of the generated environment is rendered and blended with live-action footage. Scenery generated by the software may also be used to create completely computer-generated scenes. In the case of animated movies such as Kung Fu Panda, the raw generation is assisted by hand-painting to accentuate subtle details. Environmental elements not commonly associated with landscapes, such as ocean waves, have also been handled by the software. Scenery generation is used in most 3D based video-games. These typically use either custom or purchased engines that contain their own scenery generators. For some games they tend to use a procedurally generated terrain. These typically use a form of height mapping and use of Perlin noise. This will create a grid that with one point in a 2D coordinate will create the same heightmap as it is pseudorandom, meaning it will result in the same output with the same input. This can then easily be translated into the product 3D image. These can then be changed from the editor tools in most engines if the terrain will be custom built. With recent developments neural networks can be built to create or texture the terrain based on previously suggested artwork or heightmap data. These would be generated using algorithms that have been able to identify images and similarities between them. With the info the machine can take other heightmaps and render a very similar looking image to the style image. This can be used to create similar images in example a Studio Ghibli or Van Gogh art-style. == Software == Most game engines, whether custom or proprietary, will have terrain generation built in. Some terrain generator programs include, Terragen, which can create terrain, water, atmosphere and lighting; L3DT, which provides similar functions to Terragen, and has a 2048 × 2048 resolution limit; and World Creator, which can create terrain, and is fully GPU powered. === List of 3D terrain generation software ===

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  Spike-and-slab regression

  Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). A recent and important contribution to this literature is Ishwaran & Rao (2005). == Model description == Suppose we have P possible predictors in some model. Vector γ has a length equal to P and consists of zeros and ones. This vector indicates whether a particular variable is included in the regression or not. If no specific prior information on initial inclusion probabilities of particular variables is available, a Bernoulli prior distribution is a common default choice. Conditional on a predictor being in the regression, we identify a prior distribution for the model coefficient, which corresponds to that variable (β). A common choice on that step is to use a normal prior with a mean equal to zero and a large variance calculated based on ( X T X ) − 1 {\displaystyle (X^{T}X)^{-1}} (where X {\displaystyle X} is a design matrix of explanatory variables of the model). A draw of γ from its prior distribution is a list of the variables included in the regression. Conditional on this set of selected variables, we take a draw from the prior distribution of the regression coefficients (if γi = 1 then βi ≠ 0 and if γi = 0 then βi = 0). βγ denotes the subset of β for which γi = 1. In the next step, we calculate a posterior probability for both inclusion and coefficients by applying a standard statistical procedure. All steps of the described algorithm are repeated thousands of times using the Markov chain Monte Carlo (MCMC) technique. As a result, we obtain a posterior distribution of γ (variable inclusion in the model), β (regression coefficient values) and the corresponding prediction of y. The model got its name (spike-and-slab) due to the shape of the two prior distributions. The "spike" is the probability of a particular coefficient in the model to be zero. The "slab" is the prior distribution for the regression coefficient values. An advantage of Bayesian variable selection techniques is that they are able to make use of prior knowledge about the model. In the absence of such knowledge, some reasonable default values can be used; to quote Scott and Varian (2013): "For the analyst who prefers simplicity at the cost of some reasonable assumptions, useful prior information can be reduced to an expected model size, an expected R2, and a sample size ν determining the weight given to the guess at R2." Some researchers suggest the following default values: R2 = 0.5, ν = 0.01, and π = 0.5 (parameter of a prior Bernoulli distribution).

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  Record sealing

  Record sealing is the process of making public records inaccessible to the public. In many cases, a person with a sealed record gains the legal right to deny or not acknowledge anything to do with the arrest and the legal proceedings from the case itself. Records are commonly sealed in a number of situations: Sealed birth records (typically after adoption or determination of paternity) Juvenile criminal records may be sealed Other types of cases involving juveniles may be sealed, anonymized, or pseudonymized ("impounded"); e.g., child sex offense or custody cases Cases using witness protection information may be partly sealed Cases involving trade secrets Cases involving state secrets == Filing under seal in US court == Normally, records should not be filed under seal without a court permission. However, FRCP 5.2 requires that sensitive text – like Social Security number, Taxpayer Identification Number, birthday, bank accounts, and children’s names – should be redacted off the filings made with the court and accompanying exhibits. A person making a redacted filing can file an unredacted copy under seal, or the Court can choose to order later that an additional filing be made under seal without redaction. Alternately, the filing party may ask the court’s permission to file some exhibits completely under seal. When the document is filed "under seal", it should have a clear indication for the court clerk to file it separately – most often by stamping words "Filed Under Seal" on the bottom of each page. Person making filing should also provide instructions to the court clerk that the document needs to be filed "under seal". Courts often have specific requirements to these filings in their Local Rules. == Difference from expungement == Expungement, which is a physical destruction, namely a complete erasure of one's criminal records, and therefore usually carries a higher standard, differs from record sealing, which is only to restrict the public's access to records, so that only certain law enforcement agencies or courts, under special circumstances, will have access to them. A record seal will greatly improve the chance of employment, as employers will not have access to damning records. There are occasions, like expungement, where one can truthfully state under oath that they have never been convicted before. Most of the time, a record seal has more relaxed requirements than an expungement. If an expungement is not allowed with a case, then sealing a record may be the best bet. Different states have different terms for what constitutes sealing of a record. == Cybersecurity incidents involving sealed records == Several cybersecurity incidents have demonstrated that sealed court documents are not always secure in practice, with vulnerabilities and data breaches exposing sensitive information. In January 2021, following the SolarWinds cyber attack, the U.S. Bankruptcy Court United States District Court for the District of Nevada announced that its Case Management/Electronic Case Files CM/ECF system had been potentially compromised. The judiciary stated that additional safeguards were being implemented to protect filings, and that the review of the incident and its impact was ongoing. Reports noted that the breach raised concerns about exposure of highly sensitive and sealed documents submitted through the CM/ECF system. In 2023, security researcher Jason Parker, following a tip from an activist, identified flaws in online court systems that exposed sealed records including confidential testimony and medical records through publicly accessible portals. In 2024, a cyber intrusion targeting attorneys in a civil case involving Representative Matt Gaetz led to the unauthorized access and leak of sealed depositions and related records. The breach exposed confidential testimony and financial records, some of which were later reported by news outlets, raising concerns about the security of electronically stored legal materials and the handling of sealed filings. In 2025, multiple reports confirmed that the federal judiciary's CM/ECF and PACER (law) filing system was compromised, exposing sealed indictments, confidential informant information, and other sensitive filings. Some courts temporarily reverted to paper-based filing to mitigate the risks of further disclosure. The FBI later confirmed that the breach had exposed sealed records, and investigators suspected foreign state actors were involved. == GAO publications referencing sealed records == Closed Criminal Plea and Sentencing Proceedings (1983) – Reviewed Department of Justice policies on closing plea and sentencing hearings. GAO noted that sealed transcripts should be unsealed once the reasons for closure no longer applied. Information on Plea Agreements and Settlements in Defense Procurement Fraud Cases (1992) – Examined outcomes of procurement fraud prosecutions. GAO observed that in some instances the results were sealed from public access. Military Recruiting: More Needs to Be Done to Better Screen Applicants and Detect Fraud (1999) – Investigated fraudulent enlistments in the armed forces. The report highlighted that sealed juvenile records often prevented recruiters from discovering prior offenses. Social Security Numbers: Governments Could Do More to Reduce Display in Public Records (2004) – Analyzed risks associated with SSN availability in state and local records. GAO pointed out that some categories of records, such as adoption proceedings, were sealed and less likely to expose identifiers. Social Security Numbers: Stronger Safeguards Needed to Protect Privacy (2005 testimony) – Testimony before Congress reiterating concerns over SSN exposure in public records, while noting that sealed categories (e.g., adoption) were exceptions. U.S. Supreme Court: Policies and Perspectives on Video and Audio Coverage of Appellate Court Proceedings (2016) – Surveyed appellate court policies on courtroom media coverage. The report acknowledged distinctions between public filings, confidential submissions, and sealed materials. Evictions: National Data Are Limited and Challenging to Collect (2024) – Examined nationwide eviction data. GAO reported that in some states eviction records may be sealed or expunged, limiting researchers' ability to compile datasets. DOD Fraud Risk Management: Enhanced Data and Collaboration Could Improve Efforts (2024) – Reviewed Department of Defense fraud-risk management. GAO noted that some adjudicative records in its dataset were sealed, restricting completeness of oversight data.

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  Kuaishou

  Kuaishou Technology is a Chinese publicly traded partly state-owned holding company based in Haidian District, Beijing, that was founded in 2011 by Hua Su (Chinese: 宿华) and Cheng Yixiao (Chinese: 程一笑). The company, listed on the Hong Kong Stock Exchange, is known for developing a mobile app for sharing users' short videos, a social network, and video special effects editor. The app is known as Kwai in many countries outside of China. It is also known as Snack Video in India, Pakistan and Indonesia. == Ownership and governance == Kuaishou's overseas team is led by the former CEO of the application 99, and staff from Google, Facebook, Netflix, and TikTok were recruited to lead the company's international expansion. The China Internet Investment Fund, a state-owned enterprise controlled by the Cyberspace Administration of China, holds a golden share ownership stake in Kuaishou. == History == Kuaishou is China's first short video platform that was developed in 2011 by engineer Hua Su and Cheng Yixiao. Prior to co-founding Kuaishou, Su Hua had worked for both Google and Baidu as a software engineer. The company is headquartered in Haidian District, Beijing. Kuaishou's predecessor "GIF Kuaishou" was founded in March 2011. GIF Kuaishou was a mobile app with which users could make and share GIF pictures. In 2013, Kuaishou became a short-video social platform. By 2013, the app had reached 100 million daily users. By 2019, it had exceeded 200 million active daily users. In March 2017, Kuaishou closed a US$350 million investment round that was led by Tencent. In January 2018, Forbes estimated the company's valuation to be US$18 billion. In April 2018, Kuaishou's app was briefly banned from Chinese app stores after China Central Television (CCTV) reported on the platform popularizing videos of teenage mothers. In 2019, the company announced a partnership with the People's Daily, an official newspaper of the Central Committee of the Chinese Communist Party, to help it experiment with the use of artificial intelligence in news. In June 2020, following the start of the 2020–2021 China–India skirmishes, the Government of India banned Kwai along with 58 other apps, citing "data and privacy issues". In January 2021, Kuaishou announced it was planning an initial public offering (IPO) to raise approximately US$5 billion. Kuaishou's stock completed its first day of trading at $300 Hong Kong dollars (HKD) (US$38.70), more than doubling its initial offer price, and causing its market value to rise to over $1 trillion HKD (US$159 billion). In February 2021, Kuaishou made a debut on the Hong Kong Stock Exchange, with its shares soaring by 194% at the opening. The company subsequently encountered major setbacks as a result of heightened regulatory restrictions on Chinese internet firms, which contributed to its share price falling by nearly 80% from its post-IPO peak. By December 2021, Kuaishou announced a major reorganization, including the layoff of 30% of its staff, primarily targeting mid-level employees earning an annual salary of $157,000 or more. This restructuring aimed to cut costs and mitigate financial losses. In October 2022, state-owned Beijing Radio and Television Station took a minority ownership stake in Kuaishou. In April 2024, a Financial Times article citing current and former Kuaishou employees stated that the company has been running an ageist redundancy programme known internally as "Limestone", culling workers in their mid-30s. In June 2024, Kuaishou and the Sichuan international communication center launched a branch center in São Paulo, Brazil. In June 2024, Kuaishou released its diffusion transformer text-to-video model, Kling, which they claimed could generate two minutes of video at 30 frames per second and in 1080p resolution. The model has been compared to that of OpenAI's Sora text-to-video model. It is accessible to the public on Kuaishou's video editing app KwaiCut via signing up for a waitlist with a Chinese phone number. In December 2025, Kuaishou came under a cyberattack which led to a temporary influx of violent and pornographic content. == Popularity == As of 2019, it had a worldwide user base of over 200 million, leading the "Most Downloaded" lists of the Google Play and Apple App Store in eight countries, such as Brazil, where it was introduced in 2019. Its main short-video platform competitor was Douyin, which is known as TikTok outside China. Compared to Douyin, Kuaishou is more popular with older users living outside China's Tier 1 cities. Its initial popularity came from videos of Chinese rural life. The app is particularly well known for its "rustic" aesthetic and is popular among rural people. Kuaishou also relied more on e-commerce revenue than on advertising revenue compared to its main competitor. == Reception == Kwai (as the app is called outside of China) was banned in India in 2020 along with other short video apps like TikTok. Kuaishou then released the clone SnackVideo, which was subsequently also banned. The app is one of the most popular social media platforms in Brazil, where Kuaishou partnered with creators to make telenovela style content, and appeals to football fans by working with football teams CR Flamengo and Santos FC and sponsoring the tournament Copa América. Kwai was notable in Brazil for spreading information (and misinformation) about the COVID-19 vaccine and political misinformation. === Manjiao Wenhua === "Manjiao wenhua" (慢脚文化) is a sarcasm term on Chinese internet on the unethical or illegal contents on Kuaishou. State broadcaster China Central Television (CCTV) reported that many contents are about child pregnancy. "Dating, pregnancy, bearing a child...these are strictly prohibited in the real time by a minor, but these contents can easily shown to audiences here." In addition, many students from primary or secondary schools make a pose of smoking. Wang Zhenhui (王贞会) from CUPSL stated that these kinds of bad values will give negative effects to the minors.

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