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  Cuboid (computer vision)

  In computer vision, the term cuboid is used to describe a small spatiotemporal volume extracted for purposes of behavior recognition. The cuboid is regarded as a basic geometric primitive type and is used to depict three-dimensional objects within a three dimensional representation of a flat, two dimensional image. == Production == Cuboids can be produced from both two-dimensional and three-dimensional images. One method used to produce cuboids utilizes scene understanding (SUN) primitive databases, which are collections of pictures that already contain cuboids. By sorting through SUN primitive databases with machine learning tools, computers observe the conditions in which cuboids are produced in images from SUN primitive databases and can learn to produce cuboids from other images. RGB-D images, which are RGB images that also record the depth of each pixel, are occasionally used to produce cuboids because computers no longer need to determine the depth of an object, as they typically do because depth is already recorded. Cuboid production is sensitive to changes in color and illumination, blockage, and background clutter. This means that it is difficult for computers to produce cuboids of objects that are multicolored, irregularly illuminated, or partially covered, or if there are many objects in the background. This is partially due to the fact that algorithms for producing cuboids are still relatively simple. == Usage == Cuboids are created for point cloud-based three-dimensional maps and can be utilized in various situations such as augmented reality, the automated control of cars, drones, and robots, and object detection. Cuboids allow for software to identify a scene through geometric descriptions in an “object-agnostic” fashion. Interest points, locations within images that are identified by a computer as essential to identifying the image, created from two-dimensional images can be used with cuboids for image matching, identifying a room or scene, and instance recognition. Interest points created from three dimensional images can be used with cuboids to recognize activities. This is possible because interest points aid software to focus on only the most important aspects of the images. RGB-D images and SLAM systems are used together in RGB-D SLAM systems, which are employed by Computer-aided design systems to generate point cloud-based three-dimensional maps. Most industrial multi-axis machining tools use computer-aided manufacturing and subsequently work in cuboid work spaces.

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  Digital classics

  Digital classics is the application of the tools of digital humanities to the field of classics, or more broadly to the study of the ancient world. == History == Classics was one of the first of the humanities disciplines to adopt computing approaches; the first references to the use of computing in the classical humanities date to the early 1960s, which might be surprising considering the reputation of the discipline as old-fashioned and stuffily traditionalist. Major projects such as the Thesaurus Linguae Graecae, founded in 1972, and the text collections of the Packard Humanities Institute set the trend, and there are still a significantly large number of ancient world projects among Humanities Computing projects today. Also, the success of traditional scholarly publications in digital guises, such as seen in the Bryn Mawr Classical Review, and the early adoption of hypertext in high profile projects like the Perseus Digital Library helped to legitimize computing in the study of classics in ways that has not always been the case in other areas of the humanities. This apparent paradox may be as a result of the many methodologies and different sources of evidence that classicists have always had to embrace, from literary sources and linguistics, to art history and archaeology, history, philosophy, religious theory, ancient documents such as inscriptions and papyri, and so forth. The fragmentary nature of many of the texts and languages of the ancient world, the scattered evidence from the material culture of ancient Greece and Rome, and the necessity to evaluate all these varieties of evidence in context are particularly likely to benefit from digital approaches such as databases, text markup, image manipulation and machine learning. == Digital classics projects == There are currently several major projects that aim to encourage and develop digital approaches to classical scholarship. The Stoa Consortium at the University of Kentucky distributes news of the discipline, and serves as a peer-reviewed electronic publication venue, and encourages open source approaches to digital classics. The Perseus Project is a digital library that also provides a collection of digital texts and analysis tools to the public; principally (but not exclusively) classical. Digital Classicist is another project and community which shares information and advice about the digital humanities applied to the field of classics. Epigraphy.info is an international open community pursuing a collaborative environment for digital epigraphy. The Liverpool Classics Mailing List is a project which can be subscribed to in which one receives email regarding Classics events around the world, as well as call for papers, studentships and public lectures.

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  Randomized benchmarking

  Randomized benchmarking is an experimental method for measuring the average error rates of quantum computing hardware platforms. The protocol estimates the average error rates by implementing long sequences of randomly sampled quantum gate operations. Randomized benchmarking is the industry-standard protocol used by quantum hardware developers such as IBM and Google to test the performance of the quantum operations. The original theory of randomized benchmarking, proposed by Joseph Emerson and collaborators, considered the implementation of sequences of Haar-random operations, but this had several practical limitations. The now-standard protocol for randomized benchmarking (RB) relies on uniformly random Clifford operations, as proposed in 2006 by Dankert et al. as an application of the theory of unitary t-designs. In current usage randomized benchmarking sometimes refers to the broader family of generalizations of the 2005 protocol involving different random gate sets that can identify various features of the strength and type of errors affecting the elementary quantum gate operations. Randomized benchmarking protocols are an important means of verifying and validating quantum operations and are also routinely used for the optimization of quantum control procedures. == Overview == Randomized benchmarking offers several key advantages over alternative approaches to error characterization. For example, the number of experimental procedures required for full characterization of errors (called tomography) grows exponentially with the number of quantum bits (called qubits). This makes tomographic methods impractical for even small systems of just 3 or 4 qubits. In contrast, randomized benchmarking protocols are the only known approaches to error characterization that scale efficiently as number of qubits in the system increases. Thus RB can be applied in practice to characterize errors in arbitrarily large quantum processors. Additionally, in experimental quantum computing, procedures for state preparation and measurement (SPAM) are also error-prone, and thus quantum process tomography is unable to distinguish errors associated with gate operations from errors associated with SPAM. In contrast, RB protocols are robust to state-preparation and measurement errors Randomized benchmarking protocols estimate key features of the errors that affect a set of quantum operations by examining how the observed fidelity of the final quantum state decreases as the length of the random sequence increases. If the set of operations satisfies certain mathematical properties, such as comprising a sequence of twirls with unitary two-designs, then the measured decay can be shown to be an invariant exponential with a rate fixed uniquely by features of the error model. == History == Randomized benchmarking was proposed in Scalable noise estimation with random unitary operators, where it was shown that long sequences of quantum gates sampled uniformly at random from the Haar measure on the group SU(d) would lead to an exponential decay at a rate that was uniquely fixed by the error model. Emerson, Alicki and Zyczkowski also showed, under the assumption of gate-independent errors, that the measured decay rate is directly related to an important figure of merit, the average gate fidelity and independent of the choice of initial state and any errors in the initial state, as well as the specific random sequences of quantum gates. This protocol applied for arbitrary dimension d and an arbitrary number n of qubits, where d=2n. The SU(d) RB protocol had two important limitations that were overcome in a modified protocol proposed by Dankert et al., who proposed sampling the gate operations uniformly at random from any unitary two-design, such as the Clifford group. They proved that this would produce the same exponential decay rate as the random SU(d) version of the protocol proposed in Emerson et al.. This follows from the observation that a random sequence of gates is equivalent to an independent sequence of twirls under that group, as conjectured in and later proven in. This Clifford-group approach to Randomized Benchmarking is the now standard method for assessing error rates in quantum computers. A variation of this protocol was proposed by NIST in 2008 for the first experimental implementation of an RB-type for single qubit gates. However, the sampling of random gates in the NIST protocol was later proven not to reproduce any unitary two-design. The NIST RB protocol was later shown to also produce an exponential fidelity decay, albeit with a rate that depends on non-invariant features of the error model In recent years a rigorous theoretical framework has been developed for Clifford-group RB protocols to show that they work reliably under very broad experimental conditions. In 2011 and 2012, Magesan et al. proved that the exponential decay rate is fully robust to arbitrary state preparation and measurement errors (SPAM). They also proved a connection between the average gate fidelity and diamond norm metric of error that is relevant to the fault-tolerant threshold. They also provided evidence that the observed decay was exponential and related to the average gate fidelity even if the error model varied across the gate operations, so-called gate-dependent errors, which is the experimentally realistic situation. In 2018, Wallman and Dugas et al., showed that, despite concerns raised in, even under very strong gate-dependence errors the standard RB protocols produces an exponential decay at a rate that precisely measures the average gate-fidelity of the experimentally relevant errors. The results of Wallman. in particular proved that the RB error rate is so robust to gate-dependent errors models that it provides an extremely sensitive tool for detecting non-Markovian errors. This follows because under a standard RB experiment only non-Markovian errors (including time-dependent Markovian errors) can produce a statistically significant deviation from an exponential decay The standard RB protocol was first implemented for single qubit gate operations in 2012 at Yale on a superconducting qubit. A variation of this standard protocol that is only defined for single qubit operations was implemented by NIST in 2008 on a trapped ion. The first implementation of the standard RB protocol for two-qubit gates was performed in 2012 at NIST for a system of two trapped ions

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  Digital redlining

  Digital redlining is the practice of creating and perpetuating inequities between already marginalized groups specifically through the use of digital technologies, digital content, and the internet. The concept of digital redlining is an extension of the practice of redlining in housing discrimination, a historical legal practice in the United States and Canada dating back to the 1930s where red lines were drawn on maps to indicate poor and primarily black neighborhoods that were deemed unsuitable for loans or further development, which created great economic disparities between neighborhoods. The term was popularized by Dr. Chris Gilliard, a privacy scholar, who defines digital redlining as "the creation and maintenance of tech practices, policies, pedagogies, and investment decisions that enforce class boundaries and discriminate against specific groups". Though digital redlining is related to the digital divide and techniques such as weblining and personalization, it is distinct from these concepts as part of larger complex systemic issues. It can refer to practices that create inequities of access to technology services in geographical areas, such as when internet service providers decide to not service specific geographic areas because they are perceived to be not as profitable and thus reduce access to crucial services and civic participation. It can also be used to refer to inequities caused by the policies and practices of digital technologies. For instance, with these methods inequities are accomplished through divisions that are created via algorithms which are hidden from the technology user; the use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. These algorithmic means are enabled through the use of unregulated data technologies that apply a score to individuals that statistically categorize personality traits or tendencies which are similar to a credit score but are proprietary to the technology companies and not under outside oversight. == Digital redlining and geography == While the roots of redlining lie in excluding populations based on geography, digital redlining occurs in both geographical and non-geographical contexts. An example of both contexts can be found in the charges brought against Facebook on March 28 of 2019, by the United States Department of Housing and Urban Development (HUD). HUD charged Facebook with violating the Fair Housing Act of 1968 by "encouraging, enabling, and causing housing discrimination through the company's advertising platform." HUD stated that Facebook allowed advertisers to “exclude people who live in a specified area from seeing an ad by drawing a red line around that area.” The discrimination called out by HUD included those that were racist, homophobic, ableist, and classist. Besides this example of geographically based digital redlining, HUD also charged that Facebook used profile information and designations to exclude classes of people. The charges stated: "Facebook enabled advertisers to exclude people whom Facebook classified as parents; non-American-born; non-Christian; interested in accessibility; interested in Hispanic culture; or a wide variety of other interests that closely align with the Fair Housing Act’s protected classes" Several media outlets pointed out HUDs own history of housing discrimination through redlining, the establishment of the Fair Housing Act to combat redlining, and how the digital platform was recreating this discriminatory practice. === Digital redlining within a geographical context === Although digital redlining refers to a complex and varied set of practices, it has been most commonly applied to practices with a geographical dimension. Common examples include when an internet service providers decide to not service specific geographic areas because those areas are seen to be not as profitable, resulting in discrimination against low-income communities, with resulting impacts on access to crucial services and civic participation. AT&T has faced specific scrutiny for this form of digital redlining, it has been reported that AT&T has been classist in its offerings of broadband internet service in areas that are more impoverished. Geographically based digital redlining can also apply to digital content or the distribution of goods sold online. Geographically based games such as Pokémon Go have been shown to offer more virtual stops and rewards in geographic areas that are less ethnically and racially diverse. In 2016, Amazon was rebuked for not offering their Prime same-day delivery service to many communities that were largely African American and had incomes that were beneath the national average. Even services such as email can be impacted, with many email administrators creating filters for flagging particular email messages as spam based on the geographical origin of the message. === Digital redlining based on personal identity === Although often aligned with discrimination that falls into a geographically based context digital redlining also refers to when vulnerable populations are targeted for or excluded from specific content or access to the internet in a way that harms them based on some aspect of their identity. Trade schools and community colleges, which typically have a more working class student body, have been found to block public internet content from their students where elite research institutions do not. The use of big data and analytics allow for a much more nuanced form of discrimination that can target specific vulnerable populations. For example, Facebook has been criticized for providing tools that allow advertisers to target ads by ethnic affinity and gender, effectively blocking minorities from seeing specific ads for housing and employment. In October 2019, a major class action lawsuit was filed against Facebook alleging gender and age discrimination in financial advertising. A broad array of consumers can be particularly vulnerable to digital redlining when it is used outside of a geographical context. Besides targeting vulnerable populations based on traditional and legally recognized classifications such as race, gender, age, etc., it has been shown that personal data mined and then resold by brokers can be used to target those who have been identified as suffering from Alzheimer's or dementia, or simply identified as impulse buyers or gullible. == Term distinctions == === Distinctions between weblining and digital redlining === Earlier distinctions have been made between weblining—the process of charging customers different prices based on profile information --- and internet or digital redlining, with digital redlining being focused not on pricing but access. As early as 2002 the Gale Encyclopedia of E-Commerce puts forth the distinction more in use today: weblining is the pervasive and generally accepted (or at least tolerated) practice of personalizing access to products and services in ways invisible to the user; digital redlining is when such personalized, data-driven schemes perpetuate traditional advantages of privileged demographics. As weblining has become more ubiquitous, the term has fallen out of use in favor of the more general term personalization. === Distinctions between the digital divide and digital redlining === Scholars have often drawn connections between the digital divide and digital redlining. In practice, the digital divide is seen as one of a number of impacts of digital redlining, and digital redlining is one of a number of ways in which the divide is maintained or extended. == Criticisms == A 2001 report looked to find if the reason for a gap in access to broadband internet by low-income and minority populations was due to a lack of availability or due to other factors. The report found that there was "little evidence of digital redlining based on income or black or Hispanic concentrations" but that there was mixed evidence of redlining based on areas in which Native American or Asian populations were larger.

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  CrewAI

  CrewAI is an open-source software framework and platform for building AI agents and multi-agent systems. Written primarily in Python, it is used to define artificial-intelligence agents, assign tasks to them, and coordinate their work through agent teams and workflows. The framework is associated with CrewAI Inc., a startup developing enterprise tools for automating business workflows with large language model-based agents. == History == CrewAI was first released on the Python Package Index in December 2023. The project was created by João Moura and later developed by CrewAI Inc. and open-source contributors. In October 2024, TechCrunch reported that CrewAI had raised $18 million across seed and Series A funding rounds from investors including Boldstart Ventures, Craft Ventures, Earl Grey Capital, and Insight Partners. The report also stated that Andrew Ng and HubSpot co-founder Dharmesh Shah had invested in the company. SiliconANGLE described the company as the developer of an open-source framework for building artificial-intelligence agents and reported that the funding consisted of a seed round led by Boldstart Ventures and a Series A led by Insight Partners. By late 2024, CrewAI had introduced commercial enterprise products built on top of its open-source components. TechCrunch reported that the company's enterprise offering added access controls, analytics, support, and templates for workflow automation. == Features == CrewAI is designed around groups of agents, sometimes called "crews", that can be assigned roles, goals, and tasks. The framework supports agent collaboration, task delegation, tool use, memory, and knowledge sources for retrieval-augmented generation workflows. The project describes two main building blocks: "Crews", which are used for autonomous agent collaboration, and "Flows", which are used for more controlled event-driven workflows. The framework is independent of LangChain and is released under the MIT License. It can be installed as a Python package and is commonly used with external large language model APIs or local models, depending on the developer's configuration. == Business model == CrewAI combines an open-source framework with commercial enterprise products. Its enterprise products are intended for organizations that need to build, monitor, and manage agent-based automations with additional security, observability, and administrative controls.

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  CSS box model

  In web development, the CSS box model refers to how HTML elements are modeled in browser engines and how the dimensions of those HTML elements are derived from CSS properties. It is a fundamental concept for the composition of HTML webpages. The guidelines of the box model are described by web standards World Wide Web Consortium (W3C) specifically the CSS Working Group. For much of the late-1990s and early 2000s there had been non-standard compliant implementations of the box model in mainstream browsers. With the advent of CSS2 in 1998, which introduced the box-sizing property, the problem had mostly been resolved. == Specifics == The Cascading Style Sheets (CSS) specification describes how elements of web pages are displayed by graphical browsers. Section 4 of the CSS1 specification defines a "formatting model" that gives block-level elements—such as p and blockquote—a width and height, and three levels of boxes surrounding it: padding, borders, and margins. While the specification never uses the term "box model" explicitly, the term has become widely used by web developers and web browser vendors. All HTML elements can be considered "boxes", this includes div tag, p tag, or a tag. Each of those boxes has five modifiable dimensions: the height and width describe dimensions of the actual content of the box (text, images, ...) the padding describes the space between this content and the border of the box the border is any kind of line (solid, dotted, dashed...) surrounding the box, if present the margin is the space around the border According to the CSS1 specification, released by W3C in 1996 and revised in 1999, when a width or height is explicitly specified for any block-level element, it should determine only the width or height of the visible element, with the padding, borders, and margins applied afterward. Before CSS3, this box model was known as W3C box model, in CSS3, it is known as the content-box. The total width of a box is therefore margin-left + border-left + padding-left + width + padding-right + border-right + margin-right. Similarly, the total height of a box equals margin-top + border-top + padding-top + height + padding-bottom + border-bottom + margin-bottom. For example, the following CSS code would specify the box dimensions of each block belonging to 'my-class'. Moreover, each such box will have total height 140px and width 240px. CSS3 introduced the Internet Explorer box model to the standard, known referred to as border-box. == History == Before HTML 4 and CSS, very few HTML elements supported both border and padding, so the definition of the width and height of an element was not very contentious. However, it varied depending on the element. The HTML width attribute of a table defined the width of the table including its border. On the other hand, the HTML width attribute of an image defined the width of the image itself (inside any border). The only element to support padding in those early days was the table cell. Width for the cell was defined as "the suggested width for a cell content in pixels excluding the cell padding." In 1996, CSS introduced margin, border and padding for many more elements. It adopted a definition width in relation to content, border, margin and padding similar to that for a table cell. This has since become known as the W3C box model. At the time, very few browser vendors implemented the W3C box model to the letter. The two major browsers at the time, Netscape 4.0 and Internet Explorer 4.0 both defined width and height as the distance from border to border. This has been referred to as the traditional or the Internet Explorer box model. Internet Explorer in "quirks mode" includes the content, padding and borders within a specified width or height; this results in a narrower or shorter rendering of a box than would result following the standard behavior. The Internet Explorer box model behavior was often considered a bug, because of the way in which earlier versions of Internet Explorer handle the box model or sizing of elements in a web page, which differs from the standard way recommended by the W3C for the Cascading Style Sheets language. As of Internet Explorer 6, the browser supports an alternative rendering mode (called the "standards-compliant mode") which solves this discrepancy. However, for backward compatibility reasons, all versions still behave in the usual, non-standard way by default (see quirks mode). Internet Explorer for Mac is not affected by this non-standard behavior. === Workarounds === Internet Explorer versions 6 and onward are not affected by the bug if the page contains certain HTML document type declarations. These versions maintain the buggy behavior when in quirks mode for reasons of backward compatibility. For example, quirks mode is triggered: When the document type declaration is absent or incomplete; When an HTML 3 or earlier document is encountered; When an HTML 4.0 Transitional or Frameset document type declaration is used and a system identifier (URI) is not present; When an SGML comment or other unrecognized content appears before the document type declaration Internet Explorer 6 also uses quirks mode if there is an XML declaration prior to the document type declaration. Various workarounds have been devised to force Internet Explorer versions 5 and earlier to display Web pages using the W3C box model. These workarounds generally exploit unrelated bugs in Internet Explorer's CSS selector processing in order to hide certain rules from the browser. The best known of these workarounds is the "box model hack" developed by Tantek Çelik, a former Microsoft employee who developed this idea while working on Internet Explorer for the Macintosh. It involves specifying a width declaration for Internet Explorer for Windows, and then overriding it with another width declaration for CSS-compliant browsers. This second declaration is hidden from Internet Explorer for Windows by exploiting other bugs in the way that it parses CSS rules. The implementation of these CSS “hacks” has been further complicated by the public release of Internet Explorer 7, which has had some issues fixed, but not others, causing undesired results in pages using these hacks. Box model hacks have proven unreliable because they rely on bugs in browsers' CSS support that may be fixed in later versions. For this reason, some Web developers have instead recommended either avoiding specifying both width and padding for the same element or using conditional comment and/or CSS filters to work around the box model bug in older versions of Internet Explorer. == Support for Internet Explorer's box model == Web designer Doug Bowman has said that the original Internet Explorer box model represents a better, more logical approach. Peter-Paul Koch gives the example of a physical box, whose dimensions always refer to the box itself, including potential padding, but never its content. He says that this box model is more useful for graphic designers, who create designs based on the visible width of boxes rather than the width of their content. Bernie Zimmermann says that the Internet Explorer box model is closer to the definition of cell dimensions and padding used in the HTML table model. The W3C has included a "box-sizing" property in CSS3. When box-sizing: border-box; is specified for an element, any padding or border of the element is drawn inside the specified width and height, "as commonly implemented by legacy HTML user agents". Internet Explorer 8, WebKit browsers such as Apple Safari 5.1+ and Google Chrome, Gecko-based browsers such as Mozilla Firefox 29.0 and later, Opera 7.0 and later, and Konqueror 3.3.2 and later support the CSS3 box-sizing property. Gecko browsers previous than 29.0 support the same functionality using the browser-specific -moz-box-sizing property. border-box is the default box model used in Bootstrap framework.

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  MADI

  Multichannel Audio Digital Interface (MADI) standardized as AES10 by the Audio Engineering Society (AES) defines the data format and electrical characteristics of an interface that carries multiple channels of digital audio. The AES first documented the MADI standard in AES10-1991 and updated it in AES10-2003 and AES10-2008. The MADI standard includes a bit-level description and has features in common with the two-channel AES3 interface. MADI supports serial digital transmission over coaxial cable or fibre-optic lines of 28, 56, 32, or 64 channels; and sampling rates to 96 kHz and beyond with an audio bit depth of up to 24 bits per channel. Like AES3 and ADAT Lightpipe, it is a unidirectional interface from one sender to one receiver. == Development and applications == MADI was developed by AMS Neve, Solid State Logic, Sony and Mitsubishi and is widely used in the audio industry, especially in the professional audio sector. It provides advantages over other audio digital interface protocols and standards such as AES3, ADAT Lightpipe, TDIF (Tascam Digital Interface), and S/PDIF (Sony/Philips Digital Interface). These advantages include: Support for a greater number of channels per line Use of coaxial and optical fiber media that support transmission of audio signals over 100 meters, up to 3000 meters over multi-mode and 40,000 meters over single-mode optical fiber The original specification (AES10-1991) defined the MADI link as a 56-channel transport for linking large-format mixing consoles to digital multitrack recording devices. Large broadcast studios also adopted it for routing multi-channel audio throughout their facilities. The 2003 revision (AES10-2003) adds a 64-channel capability by removing varispeed operation and supports 96 kHz sampling frequency with reduced channel capacity. The latest AES10-2008 standard includes minor clarifications and updates to correspond to the current AES3 standard. Audio over Ethernet of various types is the primary alternative to MADI for transport of many channels of professional digital audio. == Transmission format == MADI links use a transmission format similar to Fiber Distributed Data Interface (FDDI) networking. Since MADI is most often transmitted on copper links via 75-ohm coaxial cables, it more closely compares to the FDDI specification for copper-based links, called CDDI. AES10-2003 recommends using BNC connectors with coaxial cables and SC connectors with optic fibers. MADI over fibre can support a range of up to 2 km. The basic data rate is 100 Mbit/s of data using 4B5B encoding to produce a 125 MHz physical baud rate. Unlike AES3, this clock is not synchronized to the audio sample rate, and the audio data payload is padded using JK sync symbols. Sync symbols may be inserted at any subframe boundary, and must occur at least once per frame. Though the standard disassociates the transmission clock from the audio sample rate, and thus requires a separate word clock connection to maintain synchronization, some vendors do give the option of locking to parts of the transmission timing information for purposes of deriving a word clock. The audio data is almost identical to the AES3 payload, though with more channels. Rather than letters, MADI assigns channel numbers from 0–63. Frame synchronization is provided by sync symbols outside the data itself, rather than an embedded preamble sequence, and the first four time slots of each sub-channel are encoded as normal data, used for sub-channel identification: Bit 0: Set to 1 to mark channel 0, the first channel in each frame Bit 1: Set to 1 to indicate that this channel is active (contains interesting data) Bit 2: notA/B channel marker, used to mark left (0) and right (1) channels. Generally, even channels are A and odd channels are B. Bit 3: Set to 1 to mark the beginning of a 192-sample data block == Sampling frequency == The original AES10-1991 specification allowed 56 channels at sample rates from 32 to 48 kHz with an additional vari-speed range of ± 12.5%. This leads to a total range of 28 to 54 kHz. At the highest frequency, this produces a total of 56 × 32 × 54 = 96768 kbit/s, leaving 3.232% of the channel for synchronization marks and transmit clock error. The 2003 revision specifies different relations between sampling frequency and number of channels. 32 kHz to 48 kHz ± 12.5%, 56 channels; 32 kHz to 48 kHz nominal, 64 channels; 64 kHz to 96 kHz ± 12.5%, 28 channels. With a 48 kHz sampling frequency, 64 channels take 64 × 32 × 48000 = 98.304 Mbit/s. Adding the minimum 8 × 58 kbit/s of framing produces 98688 bit/s, leaving 1.312% free for timing variation and other overhead. Both versions of the standard accommodate higher sampling frequencies (for example, 96 kHz or 192 kHz) by using two or more channels per audio sample on the link.

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  Go-box

  Go-box is a name used for a number of electronic devices. The "Go-Box" is often a box, crate, carry-case, modified briefcase or similar construction containing electronic equipment pre-setup and ready to function. The box can then be taken into the field or placed at a remote site with minimal effort. These are often used by radio amateurs (or "Hams") for emergency communications, experimental work, or field communications. This has also led to similar equipment being used in the Emergency Services, utility companies, military, and government agencies. A search of the YouTube website can reveal a number of ideas for these devices mostly built by people at home. Terms created after the use of "go-box" include the "go-bag" which is an 'essentials' bag of items needed for evacuations or quick departures, i.e. medicines, clothes, torch, Broadcast radio receiver, batteries, etc. In Austria it is a radio transmitter used in trucks as part of the Videomaut toll collection system. One use of the term in the United States it is a device which is supposed to change traffic signals from red to green. U.S. Fire trucks have a similar device, called an Opticon, that uses an infrared beam. Two residents of Miami, Florida, were arrested for selling fake go-boxes online. Several hundred were sold, prices ranging from $69 to $150. In reality, the boxes contained nothing more than strobe lights.

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  Data access layer

  A data access layer (DAL) is a software architectural layer that provides access to data from one or more sources, such as a relational database, NoSQL database, SQL query engine, file system, or other persistent storage. It separates client code from the details of storage systems, query execution, connection handling, and data retrieval. Data access layers are commonly used to centralize data access logic, reduce coupling between applications and data sources, and provide a consistent interface for retrieving, writing, or querying data. Depending on the system, a data access layer may be implemented as application code, a shared library, an intermediary service, or part of a broader database abstraction layer. == In application architecture == In application software, a data access layer provides a boundary between business logic or application code and the systems used to store or retrieve data. For example, a data access layer may expose methods or interfaces for retrieving, writing, or querying data while hiding details such as connection management, SQL statements, storage APIs, error handling, and result conversion. Depending on the application, the layer may return objects, records, tabular results, documents, streams, or other representations of data. A common implementation is a set of classes, functions, or methods that directly reference database queries, stored procedures, storage APIs, or other data sources. For example, instead of using commands such as insert, delete, and update throughout an application to access a specific table, methods such as registerUser or loginUser may be implemented inside the data access layer. Business logic methods from an application can also be mapped to the data access layer. Instead of making several database queries directly, an application can call a single DAL method that abstracts those database calls. Applications using a data access layer may be either dependent on or independent from a particular database server. If the data access layer supports multiple database systems, the application can use any database system that the DAL can access. In either case, the data access layer provides a centralized location for calls into the underlying data store, which can make it easier to maintain, test, or port the application to other storage systems. == Implementation patterns == A data access layer can be implemented using several patterns and technologies, including data access objects, repositories, stored procedures, query builders, database drivers, or object–relational mapping tools. These mechanisms may implement part or all of a data access layer, but are not always equivalent to the layer itself. Object–relational mapping tools are commonly used in data access layers for object-oriented applications that map records in a relational database to objects in a programming language. Other data access layers may expose lower-level database interfaces, tabular results, document-oriented data, files, streams, or protocol-level interfaces. == Use with multiple underlying data systems == A data access layer may be used to abstract differences between multiple underlying data systems, allowing applications to access them through a more consistent interface. In such designs, applications call the DAL rather than interacting directly with each database or storage system. The layer may then handle connection management, query generation, result mapping, error handling, and other implementation details. A data access layer may be implemented as a shared library or as an intermediary service, such as a proxy or gateway. In this configuration, client applications or services connect to the data access layer, which then communicates with one or more underlying databases or query engines. This can provide a common location for authentication, authorization, logging, routing, and translation between different database interfaces. == Interfaces and protocols == Data access layers may expose or use standardized interfaces and protocols for database access. Examples include Open Database Connectivity (ODBC), Java Database Connectivity (JDBC), database-native wire protocols, and newer interfaces such as Apache Arrow Database Connectivity (ADBC) and Arrow Flight SQL. In systems that support multiple data stores, a data access layer may provide a consistent interface while using different drivers, protocols, or query mechanisms internally. == Distinction from related patterns == A data access layer is related to, but broader than, a data access object, which is usually an object-oriented design pattern for encapsulating access to a persistence mechanism. It is also related to a database abstraction layer, which focuses on hiding differences between database systems. In practice, the terms may overlap.

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  Affordable affluence

  Affordable affluence refers to a cultural phenomenon where consumers use accessible luxury goods and lifestyles to project status and align themselves with a higher social class, without requiring substantial wealth. This concept is embodied by brands such as Aritzia and Erewhon, which position themselves as offering high-end, trendy, or health-conscious products that are relatively accessible to the average consumer. A related concept is quiet luxury, where the ultra-wealthy signal wealth through subtle means. Quiet luxury emphasizes the widening gap between the ultra-wealthy and the general public, whereas accessible affluence provides a way for the general public to indulge in the lifestyle of the ultra-wealthy. == Origin of the term == An early use of the phrase in this context in a 2023 article in The Cut called "Meet the People Working 3 Jobs to Afford Erewhon." One of the interviewees used Erewhon as an archetype of affordable affluence. It was described as “a way for regular people to position themselves adjacent to the upper class.” == Background and description == The phenomenon arises due to an individual's desire to showcase status. For years, companies have strategized how to target the average consumers by providing a product that signals an elevated social status. For instance, Aritzia partnered with celebrities and micro-influencers to make it an aspirational brand at an affordable cost. Erewhon similarly has allowed middle class consumers to subtly signal a higher degree of perceived wealth by purchasing higher priced, but still attainable items. It has allowed middle-class individuals to feel as though they are part of an exclusive culture. This phenomenon has been seen particularly with Gen Z and Millennials in the setting of financial hardships in the 2020s. Affordable affluence is an example of the lipstick effect. Because traditional status symbols such as expensive cars became relatively more unattainable, posting clips on social media that showcase affordable affluence become an alternative status symbol. Particularly with food, the perception has evolved from a necessity to a luxury. A McKinsey & Company report demonstrated that these generations place a higher importance on groceries than restaurants, travel, and beauty/fashion.

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  Active networking

  Active networking is a communication pattern that allows packets flowing through a telecommunications network to dynamically modify the operation of the network. Active network architecture is composed of execution environments (similar to a unix shell that can execute active packets), a node operating system capable of supporting one or more execution environments. It also consists of active hardware, capable of routing or switching as well as executing code within active packets. This differs from the traditional network architecture which seeks robustness and stability by attempting to remove complexity and the ability to change its fundamental operation from underlying network components. Network processors are one means of implementing active networking concepts. Active networks have also been implemented as overlay networks. == What does it offer? == Active networking allows the possibility of highly tailored and rapid "real-time" changes to the underlying network operation. This enables such ideas as sending code along with packets of information allowing the data to change its form (code) to match the channel characteristics. The smallest program that can generate a sequence of data can be found in the definition of Kolmogorov complexity. The use of real-time genetic algorithms within the network to compose network services is also enabled by active networking. == How it relates to other networking paradigms == Active networking relates to other networking paradigms primarily based upon how computing and communication are partitioned in the architecture. === Active networking and software-defined networking === Active networking is an approach to network architecture with in-network programmability. The name derives from a comparison with network approaches advocating minimization of in-network processing, based on design advice such as the "end-to-end argument". Two major approaches were conceived: programmable network elements ("switches") and capsules, a programmability approach that places computation within packets traveling through the network. Treating packets as programs later became known as "active packets". Software-defined networking decouples the system that makes decisions about where traffic is sent (the control plane) from the underlying systems that forward traffic to the selected destination (the data plane). The concept of a programmable control plane originated at the University of Cambridge in the Systems Research Group, where (using virtual circuit identifiers available in Asynchronous Transfer Mode switches) multiple virtual control planes were made available on a single physical switch. Control Plane Technologies (CPT) was founded to commercialize this concept. == Fundamental challenges == Active network research addresses the nature of how best to incorporate extremely dynamic capability within networks. In order to do this, active network research must address the problem of optimally allocating computation versus communication within communication networks. A similar problem related to the compression of code as a measure of complexity is addressed via algorithmic information theory. One of the challenges of active networking has been the inability of information theory to mathematically model the active network paradigm and enable active network engineering. This is due to the active nature of the network in which communication packets contain code that dynamically change the operation of the network. Fundamental advances in information theory are required in order to understand such networks. == Nanoscale active networks == As the limit in reduction of transistor size is reached with current technology, active networking concepts are being explored as a more efficient means accomplishing computation and communication. More on this can be found in nanoscale networking.

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  Death and the Internet

  A recent extension to the cultural relationship with death is the increasing number of people who die having created a large amount of digital content, such as social media profiles, that will remain after death. This may result in concern and confusion, because of automated features of dormant accounts (e.g. birthday reminders), uncertainty of the deceased's preferences that profiles be deleted or left as a memorial, and whether information that may violate the deceased's privacy (such as email or browser history) should be made accessible to family. Issues with how this information is sensitively dealt with are further complicated as it may belong to the service provider (not the deceased) and many do not have clear policies on what happens to the accounts of deceased users. While some sites, including Facebook and X (formerly Twitter), have policies related to death, others remain dormant until if applicable, deleted due to inactivity or transferred to family or friends. The FADA (Fiduciary Access to Digital Assets Act) was set in place to make it possible to transfer digital possessions legally. More broadly, the heavy increase in social media use is affecting cultural practices surrounding death. "Virtual funerals" and other forms of previously physical memorabilia are being introduced into the digital world, complete with public details of a person's life and death. == E-mail == Gmail and Hotmail allow the email accounts of the deceased to be accessed provided certain requirements are met. Yahoo! Mail will not provide access, citing the No Right of Survivorship and Non-Transferability clause in the Yahoo! terms of service. In 2005, Yahoo! was ordered by the Probate Court of Oakland County, Michigan, to release emails of deceased US Marine Justin Ellsworth to his father, John Ellsworth. == By website == === Facebook === ==== Policies ==== In its early days, Facebook used to delete profiles of dead people, but does not anymore. In October 2009, the company introduced "memorial pages" in response to multiple user requests related to the 2007 Virginia Tech shooting. After receiving a proof of death via a special form, the profile would be converted into a tribute page with minimal personal details, where friends and family members could share their grief. In February 2015, Facebook allowed users to appoint a friend or family member as a "legacy contact" with the rights to manage their page after death. It also gave Facebook users an option to have their account permanently deleted when they die. As of January 2019, all 3 options were active. ==== Controversies ==== In 2013, BuzzFeed criticized Facebook for the lack of control over memorialization that resulted in a "Facebook death" prank aimed at locking users out of their own accounts. In 2017, Reuters reported that a German court rejected a mother's demand to access her deceased daughter's memorialized account stating that the right to private telecommunications outweighed the right to inheritance. In July 2018, Dubai's DIFC Courts ruling clarified that Facebook, Twitter and other social media accounts should be bequeathed in legally binding will. Social media networks have also been criticized for not responding to relatives' requests to alter information on memorialized accounts. Another criticism is that Facebook users often are unaware that their content is ultimately owned not by them, but by Facebook. === Dropbox === ==== Policies ==== Dropbox determines inactive accounts by looking at sign-ins, file shares, and file activity over the previous 12 months. Once an account is determined inactive, Dropbox deletes the files on the account. To request access to the account of a deceased person, heirs are required to send appropriate documents by physical mail. === Google === ==== Policies ==== In April 2013, Google announced the creation of the 'Inactive Account Manager', which allows users of Google services to set up a process in which ownership and control of inactive accounts is transferred to a delegated user. Google also allows users to submit a range of requests regarding accounts belonging to deceased users. Google works with immediate family members and representatives to close online accounts in some cases once a user is known to be deceased, and in certain circumstances may also provide content from a deceased user's account. === X (formerly Twitter) === ==== Policies ==== Until 2010, Twitter (launched in July 2006) did not have a policy on handling deceased user accounts, and simply deleted timelines of deceased users. In August 2010, Twitter allowed memorialization of accounts upon request from family members, and also provided them with an option of either deleting the account or obtaining a permanent backup of the deceased user's public tweets. In 2014, Twitter updated its policy to include an option to delete deceased user photographs. This policy was implemented after multiple Twitter trolls sent Zelda Williams, daughter of Robin Williams, photoshopped images of her father. As of January 2019, the only option that Twitter offered for the accounts of dead people was account deactivation. Previously published content is not removed. To deactivate an account Twitter requires an immediate family member to present a copy of their ID and a death certificate of the deceased. Twitter specified that it does not provide account access to anyone, but does allow people having account login information to continue posting. A prominent example is Roger Ebert's account maintained by his wife Chaz. ==== Controversies ==== In 2012, The Next Web columnist Martin Bryant noticed that since Twitter, unlike Facebook, did not have a "one account per real person" emphasis, memorializing accounts presented a difficulty to the service. He also criticized the service for the lack of control over hacking of such accounts and disapproved the practice of passing dead people's usernames to new owners after a certain period of inactivity. In 2013, Variety ran a feature about Cory Monteith's Twitter account that had 1.5 million followers at the moment on his death and gained almost 1 million new followers afterwards. Monteith's fans also launched #DontDeleteCorysTwitter campaign. As of February 2019, the celebrity's account had 1.63 million followers. Various media reported awkward incidents related to automatic posting and account hacking. === iTunes === ==== Policies ==== iCloud and iTunes accounts are "non transferable" since the content is not owned — users only have a licence to access it. === Wikipedia === Users who have made at least several hundred edits or are otherwise known for substantial contributions to Wikipedia can be noted at a central memorial page. Wikipedia user pages are ordinarily fully edit-protected after the user has died, to prevent vandalism. === YouTube === YouTube grants access to accounts of deceased persons under certain conditions. It is one of the data options that one can select to give access to a trusted contact with Google's Inactive Account Manager. === Instagram === ==== Policies ==== As of the COVID-19 pandemic, Instagram has notified its users of a delay in time of reviewing reports of deceased users due to the limited staff the pandemic has caused. Users that submit a report on a deceased user on Instagram can either memorialize the account or remove it from Instagram's platform. Through memorializing the account, Instagram secures and protects a platform of a deceased user, but per their policy, they do not supply any of the login credentials to the account. For both memorializing or removing a deceased users account, a verified user needs to submit a tangible document that shows proof of death of the user. However, to fully remove an account, the user must be a close or direct family member to the deceased person, and show proof of credibility as well. === Microsoft === ==== Policies ==== Per Microsoft's policies, they do not supply any of the login credentials to a deceased user's Microsoft account. A user does not have to contact or notify Microsoft of the deceased user, as the related user is able to close the account themselves. At default, Microsoft removes accounts after 2 years of inactivity. If the user does not have access to the deceased user's account, Microsoft recommends that the user deletes all bank accounts linked to that of the deceased to ensure no subscriptions are still going through. If the user wants to request to gain access to the deceased user's account, a court order or a subpoena has to be provided to Microsoft, but does not guarantee access to the deceased user's account. For users that live in Germany, more documentation is needed to gain access of a deceased user's account, including the deceased user's death certificate, a form of ID, and a documentation of consent from the deceased. The requesting user needs to provide a form of ID as well. == Digital inheritance == Digital inheritance is the process of handing over

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  Webull

  Webull Corporation, often stylized as simply Webull, is a U.S.-based financial services holding company headquartered in St. Petersburg, Florida. It owns and operates the Webull electronic trading platform for self-directed retail investors. Depending on jurisdiction, the Webull platform offers trading in stocks, exchange-traded funds (ETFs), options, margin, bonds, cryptocurrency and futures, as well as market-data tools. Webull began operations in 2016 under Hunan Fumi Information Technology, a China-based financial technology company founded by Wang Anquan. It launched U.S. brokerage services through Webull Financial LLC in 2018 and expanded during the retail-trading boom of 2020 and 2021. In April 2025, Webull became a publicly traded company on the Nasdaq through a merger with special-purpose acquisition company SK Growth Opportunities Corporation. The company's U.S. brokerage revenue relies substantially on payment for order flow, with options trading accounting for the larger share of its order-flow rebates in 2025. Webull has faced regulatory actions related to options customer approvals, complaint handling, suspicious activity reporting, social-media marketing and customer disclosures. It has also faced scrutiny from U.S. lawmakers and state officials over its historical and operational ties to China and the handling of U.S. customer data. == History == === Founding === Webull was founded in 2016 under Hunan Fumi Information Technology, a China-based financial technology company, by Wang Anquan, a former employee of Alibaba Group and Xiaomi. Hunan Fumi Information Technology received backing from Xiaomi, Shunwei Capital, and other investors in China. Fumi Technology was a Hunan-based fintech start-up incubated by Xiaomi and raised about CNY200 million (approximately US$30 million) in a Series B financing round in 2018. On May 24, 2017, Webull Financial LLC was established as a Delaware limited liability company. It began offering brokerage services in the United States in May 2018. Wang hired Anthony Denier as CEO of the U.S. brokerage that year and the two mapped out their strategy on napkins at a Mexican restaurant in New York City. Webull Corporation was incorporated in the Cayman Islands in September 2019 as the group's holding company. === Retail trading boom === In May 2020, the company received SEC approval to launch a robo-advisor on its platform. By August 2020, the platform had over 11 million registered users, and in October 2020, it had 750,000 daily active users. Webull introduced options trading in 2020 and later added cryptocurrency trading through a separate digital-asset business. In November 2020, Webull began supporting cryptocurrency transactions. In December 2020, Webull launched trading services in Hong Kong. During the GameStop short squeeze in January 2021, Webull gained attention as some retail traders looked for alternatives to Robinhood. On January 27, 2021, Webull recorded its highest-ever number of active daily users, at 952,000, and the Webull app was downloaded across the Apple App and Google Play stores an estimated 100,000 times. That week, approximately 1.2 million people downloaded the Webull mobile app, which the company reported as a 1,548% week-over-week increase. On January 28, 2021, Webull was directed by its clearing house to temporarily halt buy orders for stocks affected by the GameStop short squeeze. In June 2021, Webull was reported to be considering a U.S. initial public offering that could raise up to $400 million. === Restructuring and expansion === Webull restructured its China-related corporate arrangements in 2022 and later stated that Hunan Fumi was no longer affiliated with the group. In 2022 and 2023, Webull expanded in several non-U.S. markets, including Singapore, Australia, South Africa, Japan, the United Kingdom and Indonesia. In June 2023, Webull moved cryptocurrency trading to a separate app called Webull Pay. By the end of 2023, Webull had 4.3 million funded accounts and US$8.2 billion in customer assets. In January 2024, Anthony Denier was promoted to group president of Webull Corporation. In November 2024, Webull launched overnight, or extended-hours, trading, expanding the trading window of U.S. stocks for users inside and outside the United States. === SPAC merger and Nasdaq listing === On February 28, 2024, Webull agreed to go public through a business combination with SK Growth Opportunities Corporation (NASDAQ: SKGR), a special-purpose acquisition company, in a deal that valued the company at approximately US$7.3 billion. The proposed valuation drew scrutiny because of Webull's limited financial disclosure at announcement, reliance on payment for order flow and small expected public float. SK Growth shareholders approved the business combination on March 30, 2025, and the transaction closed on April 10, 2025. Webull's Class A ordinary shares and warrants began trading on the Nasdaq on April 11, 2025 under the ticker symbols BULL and BULLW (incentive warrants traded under BULLZ until their redemption in June 2025). The merger brought Webull to the public market but generated little cash for the company: after shareholder redemptions, Webull disclosed net proceeds of US$430,066 from the transaction. After the listing, Webull's shares experienced extreme volatility, rising as much as 500% to US$79.56 on April 14, 2025, after closing at US$13.25 on the prior trading day. The initial post-listing surge increased the value of Webull holdings owned by earlier investors, including RIT Capital Partners, which had first invested in Webull in 2021. In April 2026, after Webull's shares had fallen about 70% over the previous year, the company authorized a US$100 million share repurchase program. == Business model and financials == Webull provides a self-directed electronic trading platform available through mobile, desktop and web applications. Depending on jurisdiction, the platform offers trading in stocks, exchange-traded funds, options, margin, futures, fixed income products, cryptocurrency, cash management features and market data tools. In the United States, Webull Financial LLC is a registered broker-dealer and member of FINRA and the Securities Investor Protection Corporation, while Webull operates in other markets through locally licensed brokerage subsidiaries. Webull operates a commission-free or low-cost brokerage model for self-directed retail investors. In the United States, a substantial part of its trading-related revenue comes from payment for order flow, while in some non-U.S. markets the company more commonly charges commissions directly to customers. The platform is aimed at more active retail investors, including users seeking options tools, extended-hours trading and real-time market data. For 2025, Webull reported total revenue of US$571.0 million, up from US$390.2 million in 2024. Equity and option order-flow rebates accounted for US$304.1 million, or 53.3% of revenue, making order-flow rebates the company's largest reported revenue category. Interest-related income accounted for US$154.3 million, handling charge income for US$87.3 million and other revenue for US$25.3 million. Options were the larger component of the company's order-flow rebates in 2025, generating US$210.0 million compared with US$94.2 million from equities. Webull also generates revenue from interest-related activities, including margin financing, customer bank deposits, stock lending and corporate bank deposits. The company has stated that its interest-related income is affected by interest rates, customer cash balances, margin balances and demand for stock lending. The company had approximately 20 million registered users worldwide as of February 2024. As of December 31, 2025, it reported 26.8 million registered users, 5.0 million funded accounts and US$24.6 billion in customer assets. As of March 2025, Webull operated in Hong Kong, Singapore, Australia, South Africa, Japan, the United Kingdom, the United States, Indonesia, Canada, Brazil, Thailand, Malaysia and Mexico. == Marketing and sponsorships == Webull has used paid digital advertising, referral incentives, free-stock promotions, affiliate marketing and sports sponsorships to acquire customers and promote its brand. In its 2025 annual filing, the company reported marketing and branding expenses of US$152.3 million in 2023, US$138.7 million in 2024 and US$135.9 million in 2025. Webull said most of its advertising and promotion costs were related to paid search and paid social advertising, and that it had reduced free-stock promotions while shifting toward deposit- and asset-transfer-based incentives. In September 2021, BSE Global, the parent company of the Brooklyn Nets and New York Liberty, entered into a global multi-year agreement with Webull. Under the agreement, Webull became an official sponsor and online brokerage partner of the teams, with branding that included a jersey patch on Brooklyn Nets uniforms. Spo

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  Group (online social networking)

  A group (often termed as a community, e-group or club) is a feature in many social networking services which allows users to create, post, comment to and read from their own interest- and niche-specific forums, often within the realm of virtual communities. Groups, which may allow for open or closed access, invitation and/or joining by other users outside the group, are formed to provide mini-networks within the larger, more diverse social network service. Much like electronic mailing lists, they are also owned and maintained by owners, moderators, or managers, who can edit posts to discussion threads and regulate member behavior within the group. However, unlike traditional Internet forums and mailing lists, groups in social networking services allow owners and moderators alike to share account credentials between groups without having to log in to every group. == History == The rise of the World Wide Web resulted in an expansion of the varieties of methods for communication on the Internet, much of which was limited in the 1980s to discussion in newsgroups, BBS and chat rooms. While the initial rise of web-based mass communication took place in the form of early Internet forums in the mid-1990s, a few services such as MSN Groups, Yahoo! Groups and eGroups pioneered the combination of web-based mailing list archives with user profiles; by 2000, such services doubled as full-fledged mailing lists and Internet forums, allowing users to create an extremely large variety of discussion and networking mediums with comparatively sparse thresholds of complexity. Further features included chat rooms (often Java-based), image and video galleries, and group calendars. The second spurt of bullecalbel networking, one which was less dependent upon mailing list-related features and more upon Internet forum features, began in the early- to mid-2000s in the form of such services as LiveJournal, Friendster, MySpace and Facebook. These services continued the evolution of the web-based e-group as a discussion and organization medium. In the late 2000s, services such as Yammer and Micromobs further advanced e-group communication by taking advantage of microblog-style activity streams. == In virtual worlds == In Second Life, groups are centered less around discussion forums (as such, an asynchronous conferencing feature is not built into the Second Life network as of 2009) and common interest, and are more centered on maintenance of a particular geographic location inside the network. Such groups are often created by the owners of areas such as buildings, plots of land or whole islands in order to cater to the most frequent visitors and patrons of the regions. With the limited asynchronous messaging capability of Second Life, groups are also a means of mass-emailing announcements pertinent to the group, but are not completely capable of hosting discussion or deliberation of such announcement messages. == The importance of online social networking groups == Before people expanded their social life to the internet, they had small circles. These included the networks gained from rural areas or villages, such as family, friends and neighbors, and community groups such as churches. These networks represented a social safety net to support individuals. Since we have moved a huge part of our social life to the internet, online social networking groups have become a way to maintain a structure in social life. Online networking is made up by clusters of people, bounding themselves together on the World Wide Web. To be able to sort out the many different clusters we belong to we use online groups to helps us arrange and make sense of all our contacts. This sense-making is rooted within us, we sort and put people into compartments or sort by categories to make sense and try to understand our relationships to the people around us. Online social networking groups therefore enables us to do the same thing online. Online social networks have a huge impact on people’s lives. Since the social network revolution has offered people with more loose ties and diversity in their relationships, it creates both stress and opportunities. Furthermore, the Internet revolution has transformed the contact point from a household to the individual. In addition, people are in constant communication with each other due to the mobile revolution. All in all, the mentioned revolutions created a new social operating system: "networked individualism". The way that people currently connect, communicate and exchange information can be described as a form of operating system because of the similarities between the structure of computer systems and the networked individualism that has taken form in society. These structures consist of unwritten rules, norms, constraints and opportunities which are apparent for those who are part of a specific network. == Concerns == There is some research claiming that fake news is infiltrating online social networking. A recent study claimed that people exposed to fake news generally revert to their original opinion even after finding out the information they were given was false.

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  FaceApp

  FaceApp is a photo and video editing application for iOS and Android developed by FaceApp Technology Limited, a company based in Cyprus. The app generates highly realistic transformations of human faces in photographs by using neural networks. The app can transform a face to make it smile, look younger, look older, or change gender. == History == FaceApp was launched on iOS in January 2017 and on Android in February 2017. It was developed by Yaroslav Goncharov, a former executive at Yandex, and created by the Russian company Wireless Lab. == Features == There are multiple options to manipulate the photo uploaded such as editor options of adding an impression, make-up, smiles, hair colors, hairstyles, glasses, age or beards. Filters, lens blur and backgrounds along with overlays, tattoos, and vignettes are also a part of the app. The gender change transformations of FaceApp have attracted particular interest from the LGBT and transgender communities, due to their ability to realistically simulate the appearance of a person as the opposite gender. == Criticism == In 2017, FaceApp faced criticism for a "hot" filter that appeared to lighten users' skin tones, prompting accusations of racial bias. The feature was briefly renamed "spark" before being removed. Founder Yaroslav Goncharov attributed the issue to training data bias and apologized. In August of that year, more criticism arose when it featured "ethnicity filters" depicting "White", "Black", "Asian", and "Indian". The filters were immediately removed from the app. In 2019, FaceApp faced criticism over its handling of user data, including concerns that it stored users' photos on its servers and could use them for commercial purposes. Founder Yaroslav Goncharov stated that images were processed on cloud servers like Google Cloud Platform and Amazon Web Services, not transferred to Russia, and were temporarily stored only to support editing functions before being deleted. U.S. Senator Chuck Schumer raised concerns about data privacy and called for an FBI investigation.

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