AI Ease Headshot Generator

AI Ease Headshot Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Telebirr

Telebirr (Amharic: ቴሌብር) is a mobile payment service developed and was launched by Ethio telecom, the state owned telecommunication and Internet service provider in Ethiopia. It took five months to develop the end-to-end service. It facilitates the delivery of cashless transactions. The platform deployed currently has the capacity of processing up to 100 transactions per second (TPS) and can be scaled up to 1000 TPS. The service is accessible via SMS, USSD, and smartphone applications. Telebirr works in five languages. == Services == Though the service is fully accessible for any customer of Ethio telecom, the users need to register through the mobile application called Telebirr or using an authorized agent or Ethio telecom shop or Unstructured Supplementary Service Data (USSD), 127# nationally. However, Telebirr also provides a “quick registration” by using any information that already exists in Ethio telecom's system.
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Split screen (computing)

Split screen is a display technique in computer graphics that consists of dividing graphics and/or text into non-overlapping adjacent parts, typically as two or four rectangular areas. This allows for the simultaneous presentation of (usually) related graphical and textual information on a computer display. TV sports adopted this presentation methodology in the 1960s for instant replay. Non-dynamic split screens differ from windowing systems in that the latter allowed overlapping and freely movable parts of the screen (the "windows") to present both related and unrelated application data to the user. In contrast, split-screen views are strictly limited to fixed positions. The split screen technique can also be used to run two instances of an application, potentially allowing another user to interact with the second instance. == In operating systems == Split screen modes are used by mobile operating systems to enable computer multitasking similar to the window interface present in desktop operating systems. Android supports split screen view of two apps natively on all devices, while certain devices, such as Samsung Galaxy Z TriFold, support three sumultaneous views. Split screen functionality is not supported on iOS, but a similar feature called Split View is present in iPadOS, first introduced in 2015 with the first generation of iPad Pro. == In video games == The split screen feature is commonly used in non-networked, also known as couch co-op, video games with multiplayer options. In its most easily understood form, a split screen for a multiplayer video game is an audiovisual output device (usually a standard television for video game consoles) where the display has been divided into 2-4 equally sized areas (depending on number of players) so that the players can explore different areas simultaneously without being close to each other. This has historically been remarkably popular on consoles, which until the 2000s did not have access to the Internet or any other network and is less common today with modern support for networked console-to-console multiplayer. In competitive split-screen games, it is customarily considered cheating to look at another player's screen section to gain an advantage. === History === Split screen gaming dates back to at least the 1970s, with games such Drag Race (1977) from Kee Games in the arcades being presented in this format. It has always been a common feature of two or more player home console and computer games too, with notable titles being Kikstart II for 8-bit systems, a number of 16-bit racing games (such as Lotus Esprit Turbo Challenge and Road Rash II), and action/strategy games (such as Toejam & Earl and Lemmings), all employing a vertical or horizontal screen split for two player games. Xenophobe is notable as a three-way split screen arcade title, although on home platforms it was reduced to one or two screens. The addition of four controller ports on home consoles also ushered in more four-way split screen games, with Mario Kart 64 and Goldeneye 007 on the Nintendo 64 being two well known examples. In arcades, machines tended to move towards having a whole screen for each player, or multiple connected machines, for multiplayer. On home machines, especially in the first and third person shooter genres, multiplayer is now more common over a network or the internet rather than locally with split screen. Starting from the late 2000s, the presence of split screen multiplayer has largely been declining due to the increasing prevalence of online multiplayer, though TechRadar reported a resurgence of split screen due to support from independent studios and increased interest from the players.
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Certified social engineering prevention specialist

Certified Social Engineering Prevention Specialist (CSEPS) is a social engineering security-awareness training and professional certification program originally developed by Kevin Mitnick and Alexis Kasperavičius. == Course structure == The original CSEPS program was structured as a multi-module corporate security-awareness course designed to teach employees, managers, and IT personnel how social engineers manipulate human behavior to bypass technical security systems. The curriculum combined case studies, psychological analysis, attack demonstrations, pretexting exercises, and operational security scenarios. The course materials described social engineering as the exploitation of "the human factor" in information security and argued that traditional technical defenses alone were insufficient to protect organizations from deception-based attacks. The training program was divided into instructional modules covering topics such as: social engineering methodology and threat analysis intelligence gathering and reconnaissance dumpster diving pretexting elicitation technique telephone-system exploitation and caller-ID spoofing psychological influence techniques industrial espionage identity theft organizational vulnerabilities security policy development and employee awareness training The course also analyzed historical and contemporary case studies involving information theft, corporate espionage, fraudulent wire transfers, and telephone-based impersonation attacks. Training exercises required participants to analyze how attackers established credibility, manipulated trust, overcame objections, and exploited organizational procedures. According to The Wall Street Journal, CSEPS was delivered as a two-day "boot camp" course costing approximately US$1,500 per attendee. Clients reportedly included the United States Air Force and the United States Marine Corps. The certification examination included multiple-choice and written-response sections dealing with social-engineering defense scenarios and mitigation strategies. == History == In 2003, Mitnick and Kasperavičius partnered with the Florida-based IT training company Intense School Inc. to offer CSEPS classes throughout the United States. In 2020, Mitnick partnered with security-awareness training company KnowBe4, and elements of the original CSEPS material became incorporated into KnowBe4's social-engineering awareness training offerings.
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Linux color management

Linux color management has the same goal as the color management systems (CMS) for other operating systems, which is to achieve the best possible color reproduction throughout an imaging workflow from its source (camera, video, scanner, etc.), through imaging software (Digikam, darktable, RawTherapee, GIMP, Krita, Scribus, etc.), and finally onto an output medium (monitor, video projector, printer, etc.). In particular, color management attempts to enable color consistency across media and throughout a color-managed workflow. Linux color management relies on the use of accurate ICC (International Color Consortium) and DCP (DNG Color Profile) profiles describing the behavior of input and output devices, and color-managed applications that are aware of these profiles. These applications perform gamut conversions between device profiles and color spaces. Gamut conversions, based on accurate device profiles, are the essence of color management. Historically, color management was not an initial design consideration of the X Window System on which much of Linux graphics support rests, and thus color-managed workflows have been somewhat more challenging to implement on Linux than on other OS's such as Microsoft Windows or macOS. This situation is now being progressively remedied, and color management under Linux, while functional, has not yet acquired mature status. Although it is now possible to obtain a consistent color-managed workflow under Linux, certain problems still remain: The absence of a central user control panel for color settings. Some hardware devices for color calibration lack Linux drivers, firmware or accessory data. Since ICC color profiles are written to an open specification, they are compatible across operating systems. Hence, a profile produced on one OS should work on any other OS given the availability of the necessary software to read it and perform the gamut conversions. This can be used as a workaround for the lack of support for certain spectrophotometers or colorimeters under Linux: one can simply produce a profile on a different OS and then use it in a Linux workflow. Additionally, certain hardware, such as most printers and certain monitors, can be calibrated under another OS and then used in a fully color-managed workflow on Linux. The popular Ubuntu Linux distribution added initial color management in the 11.10 release (the "Oneiric Ocelot" release). == Requirements for a color-managed workflow == Accurate device profiles obtained with source or output characterization software. Correctly loaded video card lookup tables (LUTs) (or monitor profiles that do not require LUT adjustments). Color-managed applications that are configured to use a correct monitor profile and input/output profiles, with support for control over the rendering intent and black point compensation. Calibration and profiling requires: for input devices (scanner, camera, etc.) a color target which the profiling software will compare to the manufacturer-provided color values of the target. or for output devices (monitor, printer, etc.) a reading with a specific device (spectrophotometer, colorimeter or spectrocolorimeter) of the color patch values and comparing the measured values against the values originally sent for output. === Monitor calibration and profiling === One of the critical elements in any color-managed workflow is the monitor, because, at one step or another, handling and making color adaptation through imaging software is required for most images, thus the ability of the monitor to present accurate colors is crucial. Monitor color management consists of calibration and profiling. The first step, calibration, is done by adjusting the monitor controls and the output of the graphics card (via calibration curves) to match user-definable characteristics, such as brightness, white point and gamma. The calibration settings are stored in a .cal file. The second step, profiling (characterization), involves measuring the calibrated display's response and recording it in a color profile. The profile is stored in an .icc file ("ICC file"). For convenience, the calibration settings are usually stored together with the profile in the ICC file. Note that .icm files are identical to .icc files - the difference is only in the name. Seeing correct colors requires using a monitor profile-aware application, together with the same calibration used when profiling the monitor. Calibration alone does not yield accurate colors. If a monitor was calibrated before it was profiled, the profile will only yield correct colors when used on the monitor with the same calibration (the same monitor control adjustments and the same calibration curves loaded into the video card's lookup table). macOS has built-in support for loading calibration curves and installing a system-wide color profile. Windows 7 onward allows loading calibration curves, though this functionality must be enabled manually. Linux and older versions of Windows require using a standalone LUT loader. === Device profiles === ICC profiles are cross-platform and can thus be created on other operating systems and used under Linux. Monitor profiles, however, require some additional attention. Since a monitor profile depends both on the monitor itself and on the video card, a monitor profile should only be used with the same monitor and video card with which it was created. The monitor settings should not be adjusted after creating the profile. In addition, since most calibration software use LUT adjustments during calibration, the corresponding LUTs must be loaded every time the display server (X11, Wayland) is started (e.g. with every graphical login). In the unlikely case of a colorimeter being unsupported by Linux, a profile created under Windows or macOS can be used under Linux. === Display-channel lookup tables === There are two approaches to loading display channel LUTs: Create a profile that does not modify video card LUTs and thus does not require LUTs be loaded later on. Ideally, this approach would rely on DDC-capable monitors—the internal monitor settings of which are set via calibration software. Unfortunately, monitors capable of making these adjustments through DDC are not common and are generally expensive. There is only one calibration software on Linux that can interact with a DDC monitor. For mainstream monitors, a couple of options exist: BasICColor software, which works with most colorimeters on the market, allows one to adjust display output via the monitor interface, and then to choose a "Profile, do not calibrate" option. By doing this, one can create a profile that does not require video card LUT adjustments. For EyeOne devices, EyeOne Match allows the user to calibrate to "Native" gamma and white point targets, which results in the LUT adjustment curves displayed after the calibration as a simple, linear 1:1 mapping (a straight line from corner to corner). Both BasICColor and EyeOne Match do not presently run under Linux but they are capable of creating a profile that does not require LUT adjustments. Use an LUT loader to actually load the LUT adjustments contained within the profile prepared during calibration. According to the documentation, these loaders do not modify the video card LUT by itself, but achieve the same type of adjustment by modifying the X server gamma ramp. Loaders are available for Linux distributions that use X.org or XFree86—the two most popular X servers on Linux. Other X servers are not guaranteed to work with the currently available loaders. There are two LUT loaders available for Linux: Xcalib is one such loader, and although it is a command-line utility, it is quite easy to use. dispwin is a part of Argyll CMS. If, for any reason, the LUT cannot be loaded, it is still recommended to go through the initial stages of calibration where a user is asked by calibration software to make some manual adjustments to the monitor, as this will often improve display linearity and also provide information on its color temperature. This is especially recommended for CRT monitors. === Color-managed applications === In ICC-aware applications, it is important to make sure the correct profiles are assigned to devices, mainly to the monitor and the printer. Some Linux applications can auto-detect the monitor profile, while others requires that it is specified manually. Although there is no designated place to store device profiles on Linux, /usr/share/color/icc/ has become the de facto standard. Most applications running under WINE have not been fully tested for color accuracy. While 8-bpp programs can have some color resolution difficulties due to depth conversion errors, colors in higher-depth applications should be accurate, as long as those programs perform their gamut conversions based on the same monitor profile as that used for loading the LUT, granted that the corresponding LUT adjustments are loaded. == List of color-managed applications == darktabl
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Agent verification

Agent verification is activity to gain assurances that purposeful artificial constructs act in accordance with their specifications. While primitive forms of inorganic agents have been used in manufacturing for centuries, the study of artificial agents did not begin until the mid 20th century. Foundational work on such agents was closely bound with the emergence of artificial intelligence as an academic discipline. Early agents deployed for industrial control systems and in computing were often controlled by quite simple logic however, not involving artificial intelligence as such. When deployed as part of a multi-agent system, even such simple agents could require special agent orientated testing methods, as their collective behaviour was challenging to verify with traditional testing techniques. Difficulties in providing assurances that agents will not behave in dangerous ways became more prevalent after the introduction of LLM agents, especially after the rapid acceleration of their deployment in 2025. The verification of agent behaviour can be conducted by formal or informal methods. Informal verification requires less mathematical skill. But when agents are part of systems where errors have significant risks — such as danger to human life, environmental damage or major financial loss — formal verification is preferred. Both regulators and system designers themselves like formal verification as it provides a high degree of mathematical certainty. It is not however always possible to formally test all aspects of an agent based system's behaviour, especially where newer LLM based agents are concerned, due in part to their high degree of autonomy. Accordingly, agent verification for low impact deployments might be carried out only with informal methods, while for high impact deployments, it may be performed with a mix of formal and informal techniques. == Terminology == In academia, the term agent verification is often defined to mean activity concerned with gaining assurance that the agent behaves in accordance with its specification - whether by processes such as testing or simulation. 'Verification' is typically contrasted with 'validation', the latter meaning activity concerned with checking that the specification itself meets user or real world needs. Such definitions are not universally adhered to however - for example, in some workplaces and documents, the words 'verification' and 'validation' can be used synonymously. Efforts to gain confidence in Agents have intensified sharply since 2025 due to the rapid roll out of LLM agents; different terms are sometimes used in the commercial sector. Here the term 'agent verification' can be used in the same sense as it is in academia, but sometimes the same activity can be covered by more ambiguous and wider ranging terms such as 'Agent governance' , 'Agent observability' or 'AI agent policing'. == History == === Classical agents === The theoretical underpinnings for artificial (inorganic) agents emerged in the mid 20th century, with establishment of cybernetics and artificial intelligence. Oliver Selfridge's 1958 Pandemonium - A Paradigm for Learning paper was an important early theoretical contribution in establishing agent oriented architecture. Practical implementations of agents for real world applications began to become widespread in the 1990s, after the introduction of the belief–desire–intention software model (BDI), and agent-oriented programming. Pure digital agents were deployed in computer infrastructure for purposes such as monitoring, while agents connected to real-world sensors and actuators were increasingly used in industrial control systems. While the concept of artificial agents was interwoven with early artificial intelligence studies right from the start, early agents lacked general purpose reasoning capabilities, often only having simple if then logic. Even a device as simple as a thermostat, which has a sensor and a means of acting, can be considered a proto agent in this sense. Verifying the behaviours of a simple single agent system is not generally especially difficult, but it can be a different matter when several simple agents coexist in the same system. Craig Reynolds's work on boids showed that relatively complex, "intelligent" behaviour can emerge from a number of such simple agents working together in a Multi-agent system (MAS). By the 1990s, even the behaviour of a single agent system could sometimes be quite complex; in accordance with the Belief–desire–intention software model, agents could have believes that might evolve over time. Agents were increasingly introduced that were controlled by quite large decision tree models, which had new vulnerabilities to adversarial attack. It was becoming increasingly apparent that traditional software verification methods had limitations for testing such agents, or even for the more primitive type of agents when they were deployed as part of a MAS. It was the use of agents for industrial control systems, sometimes associated with robotics, that lent urgency to the practice of agent verification. Informal testing might be acceptable for digital agents used say to monitor whether each of an organisation's computers are properly licensed. But with an increasing potential for faulty agents to result in a failure that might cause a large fire to break out at a chemical manufacturing plant, a botched medical operation, or even a crashed aircraft, the need to develop reliable means of verifying behaviour of such agents was considered urgent. The Foundation for Intelligent Physical Agents was established in 1996. From the late 90s, a growing number of industry and university based scientists began working on the problem, with researchers publishing papers on the verification of both single and multi agent systems. Much of this work showed how formal verification techniques like model checking could be used to gain a high level of assurance that agent based systems would conform with their specification. A 2018 systematic review covering 231 studies found that model checking was the most common technique for agent verification, with theorem proving the second most commonly used formal verification method. In the first two decades of the 20th century, agents run by AI became more common, with Siri and Alexa being well known examples. But such agents still lacked general reasoning capabilities and did not pose new pressing problems for agent verification. === General purpose reasoning agents === The advent of LLMs created huge potential for further use of artificial agents, as agents based on them could have general purpose cognitive abilities. Agents run by LLMs (and occasionally non-LLM foundation models) have similar vulnerability to adversarial attack as those run by decision tree models. The wider scope of actions for LLM agents has created new challenges for their verification, over and above those present for classical agents. For example, the LLM's neural network endows it with infinite domains, an especial challenge for traditional formal verification techniques. Academics began to study the problems involved in verifying LLM agents from 2018. Deployment of such agents began to accelerate in late 2023 after OpenAI's "function-calling" API was made available, and especially after Anthropic's late 2024 introduction of Model Context Protocol (MCP), a standardised way for LLM agents to gain contextual awareness, and to act on the world by calling various external tools. The rapid rollout of LLM agents following MCP's release has seen the task of agent verification receive increased attention within academia, and also from the private sector. In 2024 and 2025 several startups focusing on LLM agent verification have been founded in both Europe and the US to meet growing demand. == Approaches == === Formal verification === Formal verification involves proving the correctness of some or all aspects of a system using mathematical methods. Such methods can range from manual formal proof, to verification assisted with automated theorem provers like Isabelle. For agent verification, model checking is by far the most frequently used formal verification method; for pre-LLM models it was often complemented with techniques using computation tree logic. Another common method is theorem proving. Formal verification provides a higher degree of confidence than informal methods, but it is not always used, even when it is possible. Sometimes a person or organisation developing software agents won't have the necessary skills, or may not see it as worth the effort if the agent(s) will not have the ability to cause much harm even if they malfunction. When agents are deployed in systems where errors could have serious consequences, the ability of formal verification methods to provide mathematical certainty tends to be strongly preferred by both regulators and designers themselves. But even for high impact systems, formal verificatio
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UpScrolled

UpScrolled is an Australian social media platform for microblogging and short-form online video sharing that was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi. == History == UpScrolled was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi, a Palestinian-Australian app developer. UpScrolled is backed by the Tech for Palestine incubator. In January 2026, UpScrolled saw increased attention and number of downloads after the acquisition of TikTok by a group of pro-Donald Trump US investors, including Larry Ellison, which led to calls to boycott TikTok and migrate to other apps. TikTok was alleged to be suppressing pro-Palestinian content, as well as news surrounding the killing of Alex Pretti in Minneapolis on the platform. UpScrolled subsequently climbed to the top 10 of Apple's App Store list of free apps. The app saw a reported 2,850% increase in downloads between 22 and 24 January 2026. As of 27 January 2026, UpScrolled "had been downloaded about 400,000 times in the US and 700,000 globally since launching in June 2025". The app became the most downloaded app in the Apple App store on 29 January 2026, following allegations that TikTok was suppressing videos and content opposed to Immigration and Customs Enforcement (ICE) under its new ownership. By 2 February 2026, UpScrolled had reached 2.5 million users. According to the Google Play Store and the Apple App Store, it has become the most downloaded social media app in the United States and Canada, with rising interest in the United Kingdom, France, Germany and Italy. On 14 February, UpScrolled was suspended from the Google Play Store; the suspension was reverted by 15 February. == Founder == Hijazi was born in Jordan. His parents and grandparents are from Safad, a northern Israeli city near the Lebanese border. He worked for IBM and Oracle prior to starting UpScrolled. Hijazi told Rest of World that he launched UpScrolled in response to Israel's genocide in Gaza which followed the October 7 attacks. He said, "I couldn't take it anymore. I lost family members in Gaza, and I didn't want to be complicit. So I was like, I'm done with this, I want to feel useful. I found this gap in the market, with a lot of people asking why there is no alternative to the Big Tech platforms for their content, which was getting censored." Hijazi also alleges that social media accounts that were posting pro-Palestinian content were getting shadow banned on larger platforms, and alleges that even his account was not exempt from being targeted by censors. Hijazi has further elaborated on the importance of social media independence to further the Palestinian cause. In January 2026, Web Summit Qatar announced that Hijazi would be an opening night speaker. Following the announcement, there was a surge in ticket sales for the summit. Hijazi lives in Sydney with his wife and daughter. He lost 60 family members during the Gaza war. == Features == UpScrolled's algorithm allows users to discover posts based on likes, comments, and shares with time decay and some randomness, all chronologically, with "no manipulation" according to the app's website. UpScrolled has an interface resembling a mix of Instagram and Twitter, allowing users to post and view text posts, photos, and videos. It also lets users send private messages to each other. The app is currently available for iOS and Android devices, with plans to upscale. UpScrolled does not include Israel as an option in its location selection menu. Cities such as Tel Aviv are included under "Occupied Territories of Palestine", and Palestine can also be set as the location. UpScrolled says that it is against censorship and shadow banning, and describes itself as "belong[ing] to the people who use it — not to hidden algorithms or outside agendas". Hijazi said, "The other platforms claim to be free speech platforms. But when it comes to anything on Palestine, that's a different story." UpScrolled states that it "does not tolerate hate speech, propaganda, or bad-faith behaviour, but it also refuses to silence voices quietly or without explanation". == User base and content == Al Jazeera reported that posts expressing pro-Palestinian sentiment or depicting the continued suffering in the Gaza Strip were "flooding" the app. Political and global issues such as the Gaza war are prominent. Content includes updates from the Gaza Freedom Flotilla, posts by doctors working in Gaza, video essays about Palantir’s influence within the military and calls for boycotts of Israel. It has been used by Gazans to crowdfund and record daily life. Celebrity users of UpScrolled include American labour activist Chris Smalls and actor Jacob Berger, both of whom were on the July 2025 Gaza Freedom Flotilla. Political figures have also joined UpScrolled, such as South African politician and Economic Freedom Fighters leader Julius Malema, and Islamic Revolutionary Guard Corps commander Esmail Qaani. One user said that most early users were attracted to the platform for the opportunity to criticize Zionism. The Jewish Telegraphic Agency (JTA) reported that UpScrolled was observed to be "flooded" with antisemitic and anti-Israel content, including Holocaust denial and accusations that Israel carried out the 9/11 attacks. In a statement, UpScrolled said, "Our content moderation hasn't been able to keep up with the massive rise of users this week. We're working with digital rights experts to grow our Trust & Safety team and are beefing up our content moderation to prevent this. We apologise to all impacted users, thank you for being part of Upscrolled." The Times reported in February 2026 that UpScrolled was hosting content that could potentially breach UK law, including antisemitic content and posts promoting Hamas, Hezbollah, Islamic State and Al-Qaeda, as well as footage of the 2019 Christchurch mosque shootings and content praising the perpetrators of the 2019 Halle synagogue shooting and 2018 Pittsburgh synagogue shooting. Antisemitic influencers Lucas Gage, Jake Shields, Stew Peters and Anastasia Maria Loupis have accounts on UpScrolled. UpScrolled’s policies prohibit threats, glorification of harm or support for terrorist or violent groups. Hijazi said harmful content was being uploaded to UpScrolled and the company had expanded its content moderation team and upgraded its technology infrastructure to deal with the issue. In May 2026, Moment magazine said that users had identified some antisemitic content, pornography and extremist videos on the platform. The magazine said there were gaps in content moderation due to the small size of the developer team. == Reception == In January 2026, the Council on American–Islamic Relations (CAIR) praised UpScrolled for "pledging to protect the free flow of ideas on its platform, including both support for and opposition to the Israeli government's human rights abuses." Guy Christensen, a pro-Palestinian social media celebrity, has encouraged his audience to download UpScrolled. Christensen characterized UpScrolled as having "no censorship, no ownership by billionaires who put their interests and biases onto you to control you". He compared the platform to others like TikTok, saying that Israel is behind censorship that wouldn't happen on UpScrolled. Jaigris Hodson, an associate professor of Interdisciplinary Studies at Royal Roads University in Canada, has argued that "Network effects mean that unless UpScrolled continues its explosive growth, people are unlikely to continue to choose it over the more established TikTok. At best, we might see a Twitter/X effect, which is where TikTok will host more pro-U.S. government content creators and those people who want to follow them, and UpScrolled will host more critical content creators and their followers."
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IPUMS

IPUMS, originally the Integrated Public Use Microdata Series, is the world's largest individual-level population database. IPUMS consists of microdata samples from United States (IPUMS-USA) and international (IPUMS-International) census records, as well as data from U.S. and international surveys. The records are converted into a consistent format and made available to researchers through a web-based data dissemination and analysis system. IPUMS is housed at the Institute for Social Research and Data Innovation (ISRDI), an interdisciplinary research center at the University of Minnesota, under the direction of Professor Steven Ruggles. == Description == IPUMS includes all persons enumerated in the United States censuses from 1850 to 1950 (though, the 1890 census is missing because it was destroyed in a fire) and from the American Community Survey since 2000 and the Current Population Survey since 1962. IPUMS includes household-level data for United States Censuses from 1790 to 1840, due to the first six censuses only including the name of the head of household, with tallied household totals following. IPUMS provides consistent variable names, coding schemes, and documentation across all the samples, facilitating the analysis of long-term change. IPUMS-International includes countries from Africa, Asia, Europe, and Latin America for 1960 forward. The database currently includes more than a billion individuals enumerated in 365 censuses from 94 countries around the world. IPUMS-International converts census microdata for multiple countries into a consistent format, allowing for comparisons across countries and time periods. Special efforts are made to simplify use of the data while losing no meaningful information. Comprehensive documentation is provided in a coherent form to facilitate comparative analyses of social and economic change. Additional databases in the IPUMS family include the: North Atlantic Population Project (NAPP) IPUMS National Historical Geographic Information System (NHGIS) IPUMS Health Surveys IPUMS Global Health IPUMS Time Use The Journal of American History described the effort as "One of the great archival projects of the past two decades." Liens Socio, the French portal for the social sciences, gave IPUMS the only “best site” designation that has gone to any non-French website, writing “IPUMS est un projet absolument extraordinaire...époustouflante [mind-blowing]!” The official motto of IPUMS is "use it for good, never for evil." All public IPUMS data and documentation are available online free of charge.
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Graphics address remapping table

The graphics address remapping table (GART), also known as the graphics aperture remapping table, or graphics translation table (GTT), is an I/O memory management unit (IOMMU) used by Accelerated Graphics Port (AGP) and PCI Express (PCIe) graphics cards. The GART allows the graphics card direct memory access (DMA) to the host system memory, through which buffers of textures, polygon meshes and other data are loaded. AMD later reused the same mechanism for I/O virtualization with other peripherals including disk controllers and network adapters. A GART is used as a means of data exchange between the main memory and video memory through which buffers (i.e. paging/swapping) of textures, polygon meshes and other data are loaded, but can also be used to expand the amount of video memory available for systems with only integrated or shared graphics (i.e. no discrete or inbuilt graphics processor), such as Intel HD Graphics processors. However, this type of memory (expansion) remapping has a caveat that affects the entire system: specifically, any GART, pre-allocated memory becomes pooled and cannot be utilised for any other purposes but graphics memory and display rendering. Since PCI Express, the GART is extended to the GTT (Graphics Translation Table), which act as a buffer or cache between system memory and graphics card, and in PCI Express, the GTT buffer size is changeable by the GPU driver. == Operating system support == === Windows === Support for AGP GART was added since Windows 95 OSR2. Later, support for GTT was added since Windows XP SP2 and Windows Vista. === Linux === Jeff Hartmann served as the primary maintainer of the Linux kernel's agpgart driver, which began as part of Brian Paul's Utah GLX accelerated Mesa 3D driver project. The developers primarily targeted Linux 2.4.x kernels, but made patches available against older 2.2.x kernels. Dave Jones heavily reworked agpgart for the Linux 2.6.x kernels, along with more contributions from Jeff Hartmann. === FreeBSD === In FreeBSD, the agpgart driver appeared in its 4.1 release. === Solaris === AGPgart support was introduced into Solaris Express Developer Edition as of its 7/05 release.
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Apache OpenNLP

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution. These tasks are usually required to build more advanced text processing services.
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Glow (app)

Glow is a fertility awareness and period-tracking app. It is part of a suite of mobile apps focused on women's reproductive health and childcare, which includes Eve by Glow (a dedicated period tracker), Glow Nurture (a pregnancy tracker), and Glow Baby (a baby development tracker). The Glow company also operates an online shop that sells several fertility-related products, including ovulation test strips, pregnancy tests, and wearable breast pumps. In 2024, Glow was reported to have approximately 25 million users across its various apps and community message boards. == History == Glow debuted in August 2013 as an iOS app. It was founded by Michael Huang and Max Levchin and launched with $6 million in Series A funding from venture capital firms Founders Fund and Andreesen Horowitz. In 2014, Glow raised an additional $17 million in Series B funding, with Formation 8 joining existing investors. In 2015, Glow launched Ruby, an app dedicated to sexual health. That year, Wired reported that the company had added features to their apps allowing men to monitor their fertility. Glow subsequently released an additional set of apps focused on pregnancy tracking and infant development. In 2016, Glow reported that it had a total of approximately 3 million users; by 2018, this had grown to 15 million. Vox described it as one of the “big two” period and fertility tracking apps and the one that had started the “boom” in the femtech space. == Application and features == Glow was initially described as a fertility application that applied data-driven methods to menstrual and ovulation tracking. Core features include cycle logging, ovulation prediction, and symptom tracking. The app also provides educational content related to reproductive health and childcare, as well as a set of online message boards that allow individuals to share experiences and seek peer support. == Privacy and legal issues == Glow has received significant media attention for its privacy and security practices. In 2016, Consumer Reports identified potential exploits in the Glow app that they claimed could have exposed private user data to hackers. Glow subsequently reported that it had fixed the vulnerabilities and told The Washington Post they had no evidence that user data had been compromised. In September 2020, the California Attorney General announced a settlement with Glow related to Consumer Reports’ findings, which included a $250,000 civil penalty. Following the US Supreme Court's 2022 Dobbs v. Jackson ruling, which legalized state-level bans on abortion, Glow (and other fertility trackers, such as Clue and Flo) came under additional scrutiny over concerns that user data on abortions could be reported to law enforcement. After this surge of media interest, a research team affiliated with the University of New South Wales conducted an investigation into the privacy practices of several popular fertility apps, including Glow. Their review of Glow was mixed, noting that they provided several privacy settings and de-identified sensitive data, but that user information could still be disclosed in the future if the app was sold. Glow rejected that claim, telling the Australian Associated Press that it "did not share" personal data. The company also cited several internal security measures it had implemented and its apps' offline data protection setting, which allows users to permanently delete their health-related data. == Reception == In 2014, Fast Company reported that 20,000 women had used Glow to conceive. Later that year, The Guardian included Glow Nurture on its list of the best iPhone apps of 2014. Media coverage often praised Glow's array of menstrual tracking options, although some reviews also noted that fertility apps are not birth control tools and cautioned against relying on them for that purpose. In 2019, Cosmopolitan singled Glow's community of users as one of its standout features.
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Voyages: The Trans-Atlantic Slave Trade Database

Voyages: The Trans-Atlantic Slave Trade Database is a database hosted at Rice University that aims to present all documentary material pertaining to the transatlantic slave trade. It is a sister project to African Origins. The database breaks down the kingdoms and countries that engaged in the Atlantic trade. By 2008, the project had gathered data on nearly 35,000 transatlantic slave voyages from 1501 to 1867. For each voyage they sought to establish dates, owners, vessels, captains, African visits, American destinations, numbers of slaves embarked, and numbers landed. They have been able to find much of this material for an estimated 80 percent of the entire transatlantic African slave trade. With corrections for missing voyages, the Project has estimated the entire size of the transatlantic slave trade with more comprehension, precision, and accuracy than before. They reckon that in 366 years, slaving vessels embarked about 12.5 million captives in Africa, and landed 10.7 million in the New World. A horrific discovery is a careful estimate that the Middle Passage took a toll of more than 1.8 million African lives. In this quantitative database, the numbers are enslaved people.
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GoodRx

GoodRx Holdings, Inc. is an American healthcare company that operates a telemedicine platform and free-to-use website and mobile app that track prescription drug prices in the United States and provide drug coupons for discounts on medications. GoodRx compares prescription drug prices at more than 75,000 pharmacies in the United States. The platform allows users to consult a doctor online and obtain a prescription for certain types of medications. == History == === Financial performance === GoodRx was founded in Santa Monica, California in 2011. GoodRx experienced substantial growth in net income in 2017 ($9 million), 2018 ($44 million), and 2019 ($66 million), but recorded a loss of $293.6 million in 2020 due to IPO-related expenses. In September 2020, GoodRx went public on the Nasdaq under the ticker symbol GDRX. The company priced its initial public offering at $33 per share, above the expected range of $24 to $28, raising more than $1.1 billion at an initial valuation of approximately $12.7 billion. In the first half of 2020, the company reported revenues of $257 million and net income of $55 million. GoodRx generated $745.4 million in revenue for the full year 2021, a 35.36% increase over 2020. During the first half of 2021, the company’s share price declined by 10.7%. The decline was attributed to increased competition in online pharmacy services and slower user growth. GoodRx reported full-year revenue of $766.6 million, with adjusted EBITDA reaching $213.5 million, exceeding guidance in the fourth quarter. GoodRx reported that 41% of prescriptions filled using its coupons were newly adherent, meaning they would not have been filled without the service. GoodRx reported a full-year 2023 revenue of $750.3 million, a decrease of 2.1% from 2022. However, its fourth-quarter revenue increased by 7% year-over-year. GoodRx achieved an Adjusted EBITDA of $217.4 million for the year and an Adjusted EBITDA Margin of 28.6%. In 2024, GoodRx achieved 6% revenue growth with $792.3 million for the full year and turned a net loss into a positive net income of $16.4 million. The company also demonstrated strong operational efficiency, with a 32.8% increase in full-year Adjusted EBITDA. In Q2 2025, GoodRx reported revenue of $203.1 million, a 1.2% increase from the previous year, and a net income of $12.8 million, a significant 92% jump, which resulted in a 6.3% net income margin. However, prescription transaction revenue declined by 3% due to a decrease in monthly active consumers, but this was offset by strong 32% growth in its Pharma Manufacturer Solutions business. GoodRx also saw a 7% decrease in subscription revenue. === Mergers and acquisitions === In 2019, GoodRx acquired HeyDoctor, a telemedicine company, to integrate virtual healthcare services into the platform. In 2021, a health video content producer, HealthiNation was acquired by GoodRx, which helped provide consumers with health information and offered pharmaceutical manufacturers new ways to reach relevant audiences. In April 2022, GoodRx acquired VitaCare Prescription Services from TherapeuticsMD to strengthen its pharma manufacturer solutions business. === Partnerships === In 2017, the company announced partnerships with major pharmaceutical companies to negotiate lower prescription drug costs. GoodRx has deep relationships with major pharmacy chains, including Walgreens, Walmart, CVS Caremark, and Publix, to allow customers to use GoodRx discounts and Gold benefits. GoodRx began its partnership with CVS Caremark in July 2023 to automatically apply coupons to insured CVS customers purchasing generic prescriptions at certain locations. In April 2024, GoodRx added Publix into its network, allowing GoodRx Gold members to use their cards at Publix Pharmacies. GoodRx partners with Pharmacy Benefit Management like Caremark, Express Scripts, and MedImpact to apply their savings directly to eligible insurance plans and members. GoodRx partners with companies like Affirm, Benefitfocus, and DoorDash to integrate their services that offer members discounts and financial flexibility for prescriptions. GoodRx also partners with organizations like the American Academy of Family Physicians Foundation to support broader access to care. In October 2022, GoodRx launched Provider Mode, which allows healthcare providers to use the app to compare costs of drugs for patients based on different payment methods and drug alternatives. In 2025, GoodRx partnered with Novo Nordisk to offer discounted cash-pay access to semaglutide products like Ozempic and Wegovy through its platform and participating pharmacies. == Products and services == GoodRx started its telemedicine service GoodRx Care in September 2019. It lets people talk to a licensed provider online for common issues and get prescriptions even if they don't have insurance. They also run condition-specific subscription plans that bundle online doctor visits, FDA-approved meds, and home delivery into one monthly payment. On the weight management side, GoodRx offers prescriptions for GLP-1 drugs like semaglutide through their telemedicine platform. This got a boost when the oral version of Wegovy became widely available in the US in early 2026. GoodRx works with drug makers like Novo Nordisk to make some medications (including semaglutide options) more affordable for people paying cash. The telemedicine part took off after GoodRx bought HeyDoctor in 2019 and brought their virtual care tools into the main platform. == Key people == The Santa Monica-based startup was founded in September 2011 by Trevor Bezdek and former Facebook executives Doug Hirsch and Scott Marlette. Marlette was one of the first 20 employees at Facebook and built Facebook's photo application. In 2005, Hirsch was the Vice President of Product at Facebook, working closely with Mark Zuckerberg. Bezdek and Hirsch served as co-chief executive officers until April 2023, when they stepped down from those roles and technology executive Scott Wagner was appointed interim chief executive officer. Bezdek became chair of the board, while Hirsch took on the role of chief mission officer. In December 2024, GoodRx announced that healthcare executive Wendy Barnes would become president and chief executive officer effective January 1, 2025. As of 2025, Barnes serves as the company’s CEO, while Trevor Bezdek and Scott Wagner serve as co-chairs of the board, and Doug Hirsch remains involved as a co-founder and senior executive. == Controversy == On February 25, 2020, Consumer Reports published an article stating that GoodRx shared user data—specifically, pseudonymized advertising ID numbers that companies use to track the behavior of web users across websites, the names of the drugs that users browsed, and the pharmacies where users sought to fill prescriptions—with Google, Facebook, and around twenty other Internet-based companies. A few days later, GoodRx released a statement saying that it had made changes to prevent user search data on medical conditions and pharmaceuticals from being shared with Facebook. In March 2020, GoodRx stopped sending data about user prescriptions to Facebook. On February 1, 2023, the Federal Trade Commission fined GoodRx US$1.5 million for violations of the Breach Notification Rule and the Federal Trade Commission Act for allegedly failing to obtain specific, informed, and unambiguous consent from users before disclosing health-related information to Facebook and Google. In November 2024, independent pharmacies filed at least three class action lawsuits against GoodRx and major pharmacy benefit managers. The cases, brought by independent pharmacies in California, Michigan, Pennsylvania, and Rhode Island, allege that GoodRx and the PBMs collaborated to suppress reimbursements for generic prescription drugs. They allege that agreements using GoodRx’s software suppressed reimbursements for generic drugs and violated the Sherman Antitrust Act. The suits claim the practices amount to price fixing which harms small pharmacies while benefiting PBMs and their affiliates. GoodRx settled both the 2023 FTC action and the 2025 class action lawsuit without admitting wrongdoing.
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Shopify

Shopify Inc., stylized as shopify, is a Canadian multinational e-commerce company headquartered in Ottawa, Ontario that operates a platform for retail point-of-sale systems. The company has over 5 million customers and processed US$292.3 billion in transactions in 2024, of which 57% was in the United States. Major customers include Tesla, LVMH, Nestlé, PepsiCo, AB InBev, Kraft Heinz, Lindt, Whole Foods Market, Red Bull, and Hyatt. The company's software has been praised for its ease of use and reasonable fee structure. It has been described as the "go-to e-commerce platform for startups". However, the company has faced criticism for allegedly inflating their sales data and for associating with controversial sellers. == History == === 2006: Founding === Shopify was founded in 2006 by friends Tobias Lütke, Daniel Weinand and Scott Lake after launching Snowdevil, an online store for snowboarding equipment, in 2004. Dissatisfied with the existing e-commerce products on the market, Lütke, a computer programmer by trade, instead built his own. Lütke used the open source web application framework Ruby on Rails to build Snowdevil's online store and launched it after two months of development. The Snowdevil founders launched the platform as Shopify in June 2006. Shopify created an open-source template language called Liquid, which is written in Ruby and has been used since 2006. In June 2009, Shopify launched an application programming interface (API) platform and App Store. The API allows developers to create applications for Shopify online stores and then sell them on the Shopify App Store. === 2010s === In January 2010, Shopify started its Build-A-Business competition, in which participants create a business using its commerce platform. The winners of the competition received cash prizes and mentorship from entrepreneurs, such as Richard Branson, Eric Ries and others. In April of that year, Shopify launched a free mobile app on the Apple App Store. The app allows Shopify store owners to view and manage their stores from iOS mobile devices. In December 2010, Shopify raised $7 million from a series A round from Bessemer Venture Partners, FirstMark Capital, and Felicis Ventures at a $20 million pre-money valuation. At that time, the company had annualized transaction value of $132 million. In October 2011, it raised $15 million in a Series B round. In August 2013, Shopify launched Shopify Payments in partnership with Stripe. Shopify Payments allows merchants to accept payments without requiring a third-party payment gateway. The company also announced the launch of a point of sale system to enable in-person sales in addition to online. The company received $100 million in Series C funding in December 2013. Shopify earned $105 million in revenue in 2014, twice as much as it raised the previous year. In February 2014, Shopify released "Shopify Plus" for large e-commerce businesses seeking access to additional features and support. Shopify went public via an initial public offering on May 21, 2015 raising more than $131 million. In September 2015, Amazon.com closed its Amazon Webstore service for merchants and selected Shopify as the preferred migration provider; In April 2016, Shopify announced Shopify Capital, a cash advance product. Shopify Capital was initially piloted to merchants within the US and allowed merchants to receive an advance on future earnings processed through its payment gateway. Since its launch in 2016, Shopify Capital has provided more than $5.1 billion in funding to Shopify merchants, with a maximum advance of $2 million. On June 7, 2016, Shopify launched its Shopify Plus Partners Program, to help agencies connect with evolving businesses in ecommerce space. On October 3, 2016, Shopify acquired Boltmade. In November 2016, Shopify partnered with Paystack which allowed Nigerian online retailers to accept payments from customers around the world. On November 22, 2016, Shopify launched Frenzy, a mobile app that improves flash sales. In January 2017, Shopify announced integration with Amazon that would allow merchants to sell on Amazon from their Shopify stores. In April 2017, Shopify introduced its Chip & Swipe Reader, a Bluetooth enabled debit and credit card reader for brick and mortar retail purchases. The company has since released additional technology for brick and mortar retailers, including a point-of-sale system with a Dock and Retail Stand similar to that offered by Square, and a tappable chip card reader. Shopify announced a one-click accelerated checkout feature called Shopify Pay in April 2017 as an exclusive feature for merchants using Shopify Payments as their payment processor. Customers can save their shipping and payment information for future purchases from all participating Shopify stores. In November 2017 Shopify announced Arrive, a mobile application to help customers track packages from both Shopify merchants and other e-commerce websites. In September 2018, Shopify announced plans to expand its office space in Toronto's King West neighborhood in 2022 as part of "The Well" complex, jointly owned by Allied Properties REIT and RioCan REIT. In October 2018, Shopify opened its first flagship, a physical space for business owners in Los Angeles. The space offered educational classes, coworking space, a "genius bar" for companies that use Shopify software, and workshops. Online cannabis sales in Ontario, Canada, used Shopify's software when the drug was legalized in October 2018. Shopify's software is also used for in-person cannabis sales in Ontario since becoming legal in 2019. In January 2019, Shopify announced the launch of Shopify Studios, a full-service television and film content and production house. On March 22, 2019, Shopify and email marketing platform Mailchimp ended an integration agreement over disputes involving customer privacy and data collection. In April 2019, Shopify announced an integration with Snapchat to allow Shopify merchants to buy and manage Snapchat Story ads directly on the Shopify platform. The company had previously secured similar integration partnerships with Facebook and Google. On August 14, 2019, Shopify launched Shopify Chat, a new native chat function that allows merchants to have real-time conversations with customers visiting Shopify stores online. === 2020s === In January 2020, the company announced plans to hire in Vancouver, Canada. Additionally, the effects of the COVID-19 pandemic contributed to lifting stock prices. On February 21, 2020, Shopify announced plans to join the Diem Association, known as Libra Association at the time. Also that month, Shopify Pay was rebranded as Shop Pay. In April, Arrive was rebranded as Shop, combining both customer-facing features under a single brand. In May, during the COVID-19 pandemic, Shopify announced it would shift most of its global workforce to permanent remote work. It was reported that Shopify's valuation would likely rise on the back of options it had in the company Affirm that was expecting to go public shortly. In November 2020, Shopify announced a partnership with Alipay to support merchants with cross-border payments. Shopify also provided the opportunity for users to connect Alibaba and AliExpress to Shopify through a Alibaba Dropshipping app that could be purchased through the Shopify App Store. Multiple applications launched between 2021 and 2024 allowed customers to connect their Shopify store to their Alibaba account and then import and publish your products. The integration automatically syncs inventory and orders between both platforms so that Alibaba vendors can ship directly to dropshipping customers.As a result of Affirm's January 13, 2021 IPO, Shopify's 8% stake in Affirm was worth $2 billion. About half of Shopify's C-level executives left the company in early 2021. On June 29, 2021, Shopify removed the 20% revenue share for app developers that make less than US$1 million per year. On January 18, 2022, Shopify announced a partnership with JD.com to let U.S. merchants expand their operations in China, listing their products on JD's cross-border e-commerce platform JD Worldwide. On March 22, 2022, Shopify introduced Linkpop, a product to create a branded, social marketplace through which merchants can advertise and market their products via links to be added on social media channels. The following month, Shopify, Alphabet Inc., Meta Platforms, McKinsey & Company, and Stripe, Inc. announced a $925 million advance market commitment of carbon dioxide removal (CDR) from companies that are developing CDR technology over the next 9 years. In June 2022, Shopify partnered with Twitter. As a part of the deal, Twitter announced that it would launch a sales channel app for all of Shopify's U.S. merchants through its app store. Shopify also partnered with PayPal to offer Shopify Payments to merchants in France. On July 26, 2022, Lütke announced immediate layoffs totalling roughly 10 percent of its workforce. In
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Termcap

Termcap (terminal capability) is a legacy software library and database used on Unix-like computers that enables programs to use display computer terminals in a terminal-independent manner, which greatly simplifies the process of writing portable text mode applications. It was superseded by the terminfo database used by ncurses, tput, and other programs. A termcap database can describe the capabilities of hundreds of different display terminals. This allows programs to have character-based display output, independent of the type of terminal. On-screen text editors such as vi and Emacs are examples of programs that may use termcap. Other programs are listed in the Termcap category. Access to the termcap database was usually provided by separate libraries, e.g. GNU Termcap. Examples of what the database describes: how many columns wide the display is what string to send to move the cursor to an arbitrary position (including how to encode the row and column numbers) how to scroll the screen up one or several lines how much padding is needed for such a scrolling operation. == History == Bill Joy wrote the first termcap library in 1978 for the Berkeley Unix operating system; it has since been ported to most Unix and Unix-like environments, even OS-9. Joy's design was reportedly influenced by the design of the terminal data store in the earlier Incompatible Timesharing System. == Data model == Termcap databases consist of one or more descriptions of terminals. === Indices === Each description must contain the canonical name of the terminal. It may also contain one or more aliases for the name of the terminal. The canonical name or aliases are the keys by which the library searches the termcap database. === Data values === The description contains one or more capabilities, which have conventional names. The capabilities are typed: boolean, numeric and string. The termcap library has no predetermined type for each capability name. It determines the types of each capability by the syntax: string capabilities have an "=" between the capability name and its value, numeric capabilities have a "#" between the capability name and its value, and boolean capabilities have no associated value (they are always true if specified). Applications which use termcap do expect specific types for the commonly used capabilities, and obtain the values of capabilities from the termcap database using library calls that return successfully only when the database contents matches the assumed type. === Hierarchy === Termcap descriptions can be constructed by including the contents of one description in another, suppressing capabilities from the included description or overriding or adding capabilities. No matter what storage model is used, the termcap library constructs the terminal description from the requested description, including, suppressing or overriding at the time of the request. == Storage model == Termcap data is stored as text, making it simple to modify. The text can be retrieved by the termcap library from files or environment variables. === Environment variables === The TERM environment variable contains the terminal type name. The TERMCAP environment variable may contain a termcap database. It is most often used to store a single termcap description, set by a terminal emulator to provide the terminal's characteristics to the shell and dependent programs. The TERMPATH environment variable is supported by newer termcap implementations and defines a search path for termcap files. === Flat file === The original (and most common) implementation of the termcap library retrieves data from a flat text file. Searching a large termcap file, e.g., 500 kB, can be slow. To aid performance, a utility such as reorder is used to put the most frequently used entries near the beginning of the file. === Hashed database === 4.4BSD based implementations of termcap store the terminal description in a hashed database (e.g., something like Berkeley DB version 1.85). These store two types of records: aliases which point to the canonical entry, and the canonical entry itself. The text of the termcap entry is stored literally. == Limitations and extensions == The original termcap implementation was designed to use little memory: the first name is two characters, to fit in 16 bits capability names are two characters descriptions are limited to 1023 characters. only one termcap entry with its definitions can be included, and must be at the end. Newer implementations of the termcap interface generally do not require the two-character name at the beginning of the entry. Capability names are still two characters in all implementations. The tgetent function used to read the terminal description uses a buffer whose size must be large enough for the data, and is assumed to be 1024 characters. Newer implementations of the termcap interface may relax this constraint by allowing a null pointer in place of the fixed buffer, or by hiding the data which would not fit, e.g., via the ZZ capability in NetBSD termcap. The terminfo library interface also emulates the termcap interface, and does not actually use the fixed-size buffer. The terminfo library's emulation of termcap allows multiple other entries to be included without restricting the position. A few other newer implementations of the termcap library may also provide this ability, though it is not well documented. == Obsolete features == A special capability, the "hz" capability, was defined specifically to support the Hazeltine 1500 terminal, which had the unfortunate characteristic of using the ASCII tilde character ('~') as a control sequence introducer. In order to support that terminal, not only did code that used the database have to know about using the tilde to introduce certain control sequences, but it also had to know to substitute another printable character for any tildes in the displayed text, since a tilde in the text would be interpreted by the terminal as the start of a control sequence, resulting in missing text and screen garbling. Additionally, attribute markers (such as start and end of underlining) themselves took up space on the screen. Comments in the database source code often referred to this as "Hazeltine braindamage". Since the Hazeltine 1500 was a widely used terminal in the late 1970s, it was important for applications to be able to deal with its limitations.
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Simulation noise

Simulation noise is a function that creates a divergence-free vector field. This signal can be used in artistic simulations for the purpose of increasing the perception of extra detail. The function can be calculated in three dimensions by dividing the space into a regular lattice grid. With each edge is associated a random value, indicating a rotational component of material revolving around the edge. By following rotating material into and out of faces, one can quickly sum the flux passing through each face of the lattice. Flux values at lattice faces are then interpolated to create a field value for all positions. Perlin noise is the earliest form of lattice noise, which has become very popular in computer graphics. Perlin Noise is not suited for simulation because it is not divergence-free. Noises based on lattices, such as simulation noise and Perlin noise, are often calculated at different frequencies and summed together to form band-limited fractal signals. Other approaches developed later that use vector calculus identities to produce divergence free fields, such as "Curl-Noise" as suggested by Rook Bridson, and "Divergence-Free Noise" due to Ivan DeWolf. These often require calculation of lattice noise gradients, which sometimes are not readily available. A naive implementation would call a lattice noise function several times to calculate its gradient, resulting in more computation than is strictly necessary. Unlike these noises, simulation noise has a geometric rationale in addition to its mathematical properties. It simulates vortices scattered in space, to produce its pleasing aesthetic. == Curl noise == The vector field is created as follows, for every point (x,y,z) in the space a vector field G is created, every component x, y and z of the vector field (Gx, Gy, Gz) is defined by a 3D perlin or simplex noise function with x, y and z as parameters. The partial derivative of Gx, Gy, and Gz respect to x, y and z is obtained with the gradient of the perlin or simplex noise by finite differences of implicit calculation inside the simplex noise. The partial derivatives are used to calculate F as the curl of G given by F = ( ∂ G z ∂ y − ∂ G y ∂ z , ∂ G x ∂ z − ∂ G z ∂ x , ∂ G y ∂ x − ∂ G x ∂ y ) {\displaystyle F=({\frac {\partial Gz}{\partial y}}-{\frac {\partial Gy}{\partial z}},{\frac {\partial Gx}{\partial z}}-{\frac {\partial Gz}{\partial x}},{\frac {\partial Gy}{\partial x}}-{\frac {\partial Gx}{\partial y}})} == Bitangent noise == This method is based in the fact that the curl of the gradient of scalar field is zero and the identity that expand the divergence of a cross product of two vectors A and B as the difference of the dot products of each vector with the curl of the other: ∇ × ( ∇ φ ) = 0 . {\displaystyle \nabla \times (\nabla \varphi )=\mathbf {0} .} ∇ ⋅ ( A × B ) = ( ∇ × A ) ⋅ B − A ⋅ ( ∇ × B ) {\displaystyle \nabla \cdot (\mathbf {A} \times \mathbf {B} )=\ (\nabla {\times }\mathbf {A} )\cdot \mathbf {B} \,-\,\mathbf {A} \cdot (\nabla {\times }\mathbf {B} )} which means that if the curl of both vector fields is zero then the divergence of the product of two vectors that are the gradients of scalar fields is zero too. This result in a divergence free vector field by construction only calling two noise functions to create the scalar fields. The vector field es created as follows, two scalar fields are calculated ϕ {\displaystyle \phi } and ψ {\displaystyle \psi } using 3D perlin or simplex noise functions, then the gradients A and B of each of this fields is calculated, the cross product of A and B gives a divergence free vector field. == Signed distance noise == The vector field is created based on a closed and differentiable implicit surface S = F(x,y,z) = 0. For every point in the space, frequently outside or near the surface, we get a vector g that is normal to the surface, this is the gradient of S or the partial derivatives respect to x, y and z, this vector is not unitary, but we can get a unitary normal n by dividing each component of the point by the magnitude of the gradient g. Outside of the surface all these normals point away from the surface. g = ∇ F ( x , y , z ) = ( ∂ F ∂ x , ∂ F ∂ y , ∂ F ∂ z ) {\displaystyle g=\nabla F(x,y,z)=\left({\frac {\partial F}{\partial x}},{\frac {\partial F}{\partial y}},{\frac {\partial F}{\partial z}}\right)} n = g ( x , y , z ) ‖ ∇ F ( x , y , z ) ‖ {\displaystyle \mathbf {n} ={\frac {g(x,y,z)}{\|\nabla F(x,y,z)\|}}} ‖ ∇ F ( x , y , z ) ‖ = ( ∂ F ∂ x ) 2 + ( ∂ F ∂ y ) 2 + ( ∂ F ∂ z ) 2 {\displaystyle \|\nabla F(x,y,z)\|={\sqrt {\left({\frac {\partial F}{\partial x}}\right)^{2}+\left({\frac {\partial F}{\partial y}}\right)^{2}+\left({\frac {\partial F}{\partial z}}\right)^{2}}}} Afterwards we calculate a scalar value p for that point in the space using a 3D perlin or simplex noise function. Now we create a vector field V = pn pointing outside of the surface. The curl of this vector field gives the direction in every point in the space where the particles should move. S D N = ( ∂ V z ∂ y − ∂ V y ∂ z , ∂ V x ∂ z − ∂ V z ∂ x , ∂ V y ∂ x − ∂ V x ∂ y ) {\displaystyle SDN=({\frac {\partial Vz}{\partial y}}-{\frac {\partial Vy}{\partial z}},{\frac {\partial Vx}{\partial z}}-{\frac {\partial Vz}{\partial x}},{\frac {\partial Vy}{\partial x}}-{\frac {\partial Vx}{\partial y}})} By construction this vector SDN will point in a tangent direction to an isosurface at the level of the signed distance to the original surface and can be used to confine the movements of the particles to stay in that surface.
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