Traité de documentation: le livre sur le livre, théorie et pratique is a landmark book by Belgian author Paul Otlet, first published in 1934. == Legacy == The book is considered a landmark in the history of information science, with concepts predicting the rise of the World Wide Web and search engines. In [Otlet's] most famous publication of 1934, Traité de Documentation, he wrote of a desk in the form of a wheel from which different projects (workspaces) could be switched as they rotated — foreshadowing the multiple desktops and tabs of contemporary computer interfaces. Inspired by the arrival of radio, phonograph, cinema, and television, Otlet also posited that there were as yet many “inventions to be discovered,” including the reading and annotation of remote documents and computer speech.
Powerset (company)
Powerset was an American company based in San Francisco, California, that, in 2006, was developing a natural language search engine for the Internet. On July 1, 2008, Powerset was acquired by Microsoft for an estimated $100 million (~$143 million in 2024). Powerset was working on building a natural language search engine that could find targeted answers to user questions (as opposed to keyword based search). For example, when confronted with a question like "Which U.S. state has the highest income tax?", conventional search engines ignore the question phrasing and instead do a search on the keywords "state", "highest", "income", and "tax". Powerset on the other hand, attempts to use natural language processing to understand the nature of the question and return pages containing the answer. The company was in the process of "building a natural language search engine that reads and understands every sentence on the Web". The company has licensed natural language technology from PARC, the former Xerox Palo Alto Research Center. On May 11, 2008, the company unveiled a tool for searching a fixed subset of English Wikipedia using conversational phrases rather than keywords. Acquisition by Microsoft: One significant milestone in Powerset's history was its acquisition by Microsoft on July 1, 2008, for an estimated $100 million. This acquisition was part of Microsoft's broader strategy to enhance its search capabilities and compete more effectively with other search engine providers, particularly Google. Natural Language Search Engine: Powerset's primary focus was on developing a natural language search engine capable of understanding and interpreting user queries in a more human-like manner. Instead of simply matching keywords, Powerset aimed to comprehend the meaning behind the words, allowing for more accurate and contextually relevant search results. Technology and Partnerships: Powerset had licensed natural language technology from PARC, the Xerox Palo Alto Research Center. This technology likely played a crucial role in the development of Powerset's NLP capabilities. Wikipedia Search Tool: In May 2008, Powerset unveiled a search tool that allowed users to search a fixed subset of English Wikipedia using conversational phrases rather than traditional keywords. This demonstrated the potential of Powerset's NLP technology in providing more precise and relevant search results. == Powerlabs == In a form of beta testing, Powerset opened an online community called Powerlabs on September 17, 2007. Business Week said: "The company hopes the site will marshal thousands of people to help build and improve its search engine before it goes public next year." Said The New York Times: "[Powerset Labs] goes far beyond the 'alpha' or 'beta' testing involved in most software projects, when users put a new product through rigorous testing to find its flaws. Powerset doesn’t have a product yet, but rather a collection of promising natural language technologies, which are the fruit of years of research at Xerox PARC." Powerlabs' initial search results are taken from Wikipedia. == Notable people == Barney Pell (born March 18, 1968, in Hollywood, California) was co-founder and CEO of Powerset. Pell received his Bachelor of Science degree in symbolic systems from Stanford University in 1989, where he graduated Phi Beta Kappa and was a National Merit Scholar. Pell received a PhD in computer science from Cambridge University in 1993, where he was a Marshall Scholar. He has worked at NASA, as chief strategist and vice president of business development at StockMaster.com (acquired by Red Herring in March, 2000) and at Whizbang! Labs. Prior to joining Powerset, Pell was an Entrepreneur-in-Residence at Mayfield Fund, a venture capital firm in Silicon Valley. Pell is also a founder of Moon Express, Inc., a U.S. company awarded a $10M commercial lunar contract by NASA and a competitor in the Google Lunar X PRIZE. Steve Newcomb was the COO and co-founder of Powerset. Prior to joining Powerset, he was a co-founder of Loudfire, General Manager at Promptu, and was on the board of directors at Jaxtr. He left Powerset in October 2007 to form Virgance, a social startup incubator. Lorenzo Thione (born in Como, Italy) was the product architect and co-founder of Powerset. Prior to joining Powerset, he worked at FXPAL in natural language processing and related research fields. Thione earned his master's degree in software engineering from the University of Texas at Austin. Ronald Kaplan, former manager of research in Natural Language Theory and Technology at PARC, served as the company's CTO and CSO. Ryan Ferrier is a member of the founding team of Powerset. He managed personnel and internal operations. After 2008 he went on to co-found Serious Business, which made Facebook applications and was later bought by Zynga. Another Powerset alumnus, Alex Le, became CTO of Serious Business and went on to become an executive producer at Zynga when it bought the company. Siqi Chen founded a stealth startup in mobile computing after leaving Powerset. Tom Preston-Werner worked at Powerset and left after the acquisition to found GitHub. == Investors == Powerset attracted a wide range of investors, many of whom had considerable experience in the venture capital field. The company received $12.5 million (~$18.2 million in 2024) in Series A funding during November 2007, co-led by the venture capital firms Foundation Capital and The Founders Fund. Among the better-known investors: Esther Dyson, founding chairman of ICANN, founder of the newsletter Release 1.0 and editor at Cnet Peter Thiel, founder and former CEO of PayPal Luke Nosek, founder of PayPal Todd Parker. Managing Partner, Hidden River Ventures Reid Hoffman, executive vice president of PayPal and founder of LinkedIn First Round Capital, seed-stage venture firm
Internet Security Awareness Training
Internet Security Awareness Training (ISAT) is the training given to members of an organization regarding the protection of various information assets of that organization. ISAT is a subset of general security awareness training (SAT). Even small and medium enterprises are generally recommended to provide such training, but organizations that need to comply with government regulations (e.g., the Gramm–Leach–Bliley Act, the Payment Card Industry Data Security Standard, Health Insurance Portability and Accountability Act, Sarbanes–Oxley Act) normally require formal ISAT for annually for all employees. Often such training is provided in the form of online courses. ISAT, also referred to as Security Education, Training, and Awareness (SETA), organizations train and create awareness of information security management within their environment. It is beneficial to organizations when employees are well trained and feel empowered to take important actions to protect themselves and organizational data. The SETA program target must be based on user roles within organizations and for positions that expose the organizations to increased risk levels, specialized courses must be required. == Coverage == There are general topics to cover for the training, but it is necessary for each organization to have a coverage strategy based on its needs, as this will ensure the training is practical and captures critical topics relevant to the organization. As the threat landscape changes very frequently, organizations should continuously review their training programs to ensure relevance with current trends. Topics covered in ISAT include: Appropriate methods for protecting sensitive information on personal computer systems, including password policy Various computer security concerns, including spam, malware, phishing, social engineering, etc. Consequences of failure to properly protect information, including potential job loss, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal law penalties. Being Internet Security Aware means you understand that there are people actively trying to steal data that is stored within your organization's computers. (This often focuses on user names and passwords, so that criminal elements can ultimately get access to bank accounts and other high-value IT assets.) That is why it is important to protect the assets of the organization and stop that from happening. The general scope should include topics such as password security, Email phishing, Social engineering, Mobile device security, Sensitive data security, and Business communications. In contrast, those requiring specialized knowledge are usually required to take technical and in-depth training courses. Suppose an organization determines that it is best to use one of the available training tools on the market, it must ensure it sets objectives that the training can meet, including confirming the training will provide employees with the knowledge to understand risks and the behaviors needed in managing them, actions to take to prevent or detect security incidents, using language easily understandable by the trainees, and ensuring the pricing is reasonable. Organizations are recommended to base ISAT training content on employee roles and their culture; the policy should guide that training for all employees and gave the following as examples of sources of reference materials: National Institute of Standards and Technology (NIST) Special Publication 800-50, Building an Information Technology Security Awareness and Training Program International Standards Organization (ISO) 27002:2013, Information technology—Security techniques—Code of practice for information security controls International Standards Organization (ISO) 27001:2013, Information technology — Security techniques — Information security management systems COBIT 5 Appendix F.2, Detailed Guidance: Services, Infrastructure and Applications Enabler, Security Awareness The training must focus on current threats specific to an organization and the impacts if that materializes as a result of user actions. Including practical examples and ways of dealing with scenarios help users know the appropriate measures to take. It is a good practice to periodically train customers of specific organizations on threats they face from people with malicious intentions. Coverage strategy for SAT should be driven by an organization's policy. It can help truly determine the level of depth of the training and where it should be conducted at a global level or business unit level, or a combination of both. A policy also empowers a responsible party within the organization to run the training. == Importance == Studies show that well-structured security awareness training can significantly reduce the likelihood of cyber incidents caused by human error. According to the Ponemon Institute, organizations that implement regular security training experience up to 70% fewer successful phishing attacks. Additionally, a 2023 Verizon Data Breach Investigations Report found that 74% of breaches involve the human element, highlighting the need for continuous education. Employees are key in whether organizations are breached or not; there must be a policy on creating awareness and training them on emerging threats and actions to take in safeguarding sensitive information and reporting any observed unusual activity within the corporate environment. Research has shown that SAT has helped reduce cyber-attacks within organizations, especially when it comes to phishing, as trainees learned to identify these attack modes and give them the self-assurance to take action appropriately. There is an increase in phishing attacks, and it has become increasingly important for people to understand how to these attacks work, and the actions required to prevent these and SAT has shown a significant impact on the number of successful phishing attacks against organizations. == Compliance Requirements == Various regulations and laws mandate SAT for organizations in specific industries, including the Gramm–Leach–Bliley Act (GLBA) for the financial services, the Federal Information Security Modernization Act of 2014 for federal agencies, and the European Union's General Data Protection Regulation (GDPR). === Federal Information Security Modernization Act === Employees and contractors in federal agencies are required to receive Security Awareness Training annually, and the program needs to address job-related information security risks linked that provide them with the knowledge to lessen security risks. === Health Insurance Portability and Accountability Act === The Health Insurance Portability and Accountability Act has the Security Rule, and Privacy Rule requiring the creation of a security awareness training program and ensuring employees are trained accordingly. === Payment Card Industry Data Security Standard === The Payment Card Industry Security Standards Council, the governing council for stakeholders in the payment industry, formed by American Express, Discover, JCB International, MasterCard, and Visa that developed the DSS as a requirement for the payment industry. Requirement 12.6 requires member organizations to institute a formal security awareness program. There is a published guide for organizations to adhere to when setting up the program. === US States Training Regulations === Some States mandate Security Awareness Training whiles other do not but simply recommend voluntary training. Among states that require the training for its employees include: Colorado (The Colorado Information Security Act, Colorado Revised Statutes 24-37.5-401 et seq.) Connecticut (13 FAM 301.1-1 Cyber Security Awareness Training (PS800)) Florida (Florida Statutes Chapter 282) Georgia (Executive Order GA E.O.182 mandated training within 90 days of issue) Illinois (Cook County) Indiana (IN H 1240) Louisiana (Louisiana Division of Administration, Office of Technology Services p. 52: LA H 633) Maryland (20-07 IT Security Policy) Montana (Mandatory cyber training for executive branch state employees) Nebraska Nevada (agency-by-agency state employee requirement - State Security Standard 123 – IT Security) New Hampshire New Jersey ( NJ A 1654) North Carolina Ohio (IT-15 - Security Awareness and Training) Pennsylvania Texas Utah Vermont Virginia West Virginia (WV Code Section 5A-6-4a) == Training Techniques == Below are some common training techniques, even though some can be blended depending on the operating environment: Interactive video training – This technique allows users to be trained using two-way interactive audio and video instruction. Web-based training – This method allows employees or users to take the training independently and usually has a testing component to determine if learning has taken place. If not, users can be allowed to retake the course and test to ensure there is a complete understanding
Comparison of operating systems
These tables provide a comparison of operating systems, of computer devices, as listing general and technical information for a number of widely used and currently available PC or handheld (including smartphone and tablet computer) operating systems. The article "Usage share of operating systems" provides a broader, and more general, comparison of operating systems that includes servers, mainframes and supercomputers. Because of the large number and variety of available Linux distributions, they are all grouped under a single entry; see comparison of Linux distributions for a detailed comparison. There is also a variety of BSD and DOS operating systems, covered in comparison of BSD operating systems and comparison of DOS operating systems. == Nomenclature == The nomenclature for operating systems varies among providers and sometimes within providers. For purposes of this article the terms used are; kernel In some operating systems, the OS is split into a low level region called the kernel and higher level code that relies on the kernel. Typically the kernel implements processes but its code does not run as part of a process. hybrid kernel monolithic kernel Nucleus In some operating systems there is OS code permanently present in a contiguous region of memory addressable by unprivileged code; in IBM systems this is typically referred to as the nucleus. The nucleus typically contains both code that requires special privileges and code that can run in an unprivileged state. Typically some code in the nucleus runs in the context of a dispatching unit, e.g., address space, process, task, thread, while other code runs independent of any dispatching unit. In contemporary operating systems unprivileged applications cannot alter the nucleus. License and pricing policies vary widely among different systems. Among others, the tables below use the following terms: BSD BSD licenses are a family of permissive free software licenses, imposing minimal restrictions on the use and distribution of covered software. bundled The fee is included in the price of the hardware == General information == == Technical information == == Security == == Commands == For POSIX compliant (or partly compliant) systems like FreeBSD, Linux, macOS or Solaris, the basic commands are the same because they are standardized. NOTE: Linux systems may vary by distribution which specific program, or even 'command' is called, via the POSIX alias function. For example, if you wanted to use the DOS dir to give you a directory listing with one detailed file listing per line you could use alias dir='ls -lahF' (e.g. in a session configuration file).
MyRadar
MyRadar is a free weather forecasting application developed by Andy Green and his Orlando, Florida-based company ACME AtronOmatic (ACME). The app began operations in 2008 and ran on government-provided weather and radar data for its first decade. In 2019, ACME launched personal satellites to improve predictions of ongoing weather. The app received funding to improve its radar and imaging from the Federal Communications Commission (FCC), National Oceanic and Atmospheric Administration (NOAA), and the Office of Naval Research (ONR). ACME created a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), which utilizes machine learning and artificial intelligence (AI) to create a current weather map. With the introduction of additional features, including the detection of wildfires and illegal fishing, the app has more broadly become an environmental intelligence app since 2022. In 2024, the app partnered with the Total Traffic and Weather Network (TTWN) to provide traffic flow and incident data for users with paying subscriptions via CarPlay and Android Auto. == History == The app's creator, Andy Green, had created internet tech since the 1980s. His first major project was the development of a public access internet service company based in Rhode Island, which he later sold to finance the creation of ACME AtronOmatic ("ACME" for short), based in Orlando, Florida. The first major app created by ACME was called "Flightwise", which provided users with flight tracking information. In summer 2008, Green had the idea to use the animated location tracker already built-in to Flightwise to make a stand-alone weather forecasting app after wondering if a meal he was eating outdoors would get rained out. MyRadar was launched in 2012 out of an office in Orlando. Despite running solely off of free government-provided weather and radar data for the first decade after launch, Green said the app "took off like wildfire" in downloads. In December 2017, the app partnered with "TripIt" to provide users with information about flight delays and gate changes, eliminating the need for a separate app like Flightwise. In 2019, ACME launched their first personal satellite for the app, a small prototype from New Zealand, as part of an effort to provide detailed imagery and improved predictions of ongoing weather unique to the app. More satellites were eventually launched by ACME to create a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), monitored by ground stations maintained by Kongsberg Satellite Services. HORIS operates MyRadar by taking the environmental data and imagery it collects and pairing it with machine learning and artificial intelligence (AI) to create a real-time weather map. In 2022, HORIS was expanded upon after ACME won approval from the Federal Communications Commission (FCC) to improve their satellite constellation to include 250 satellites or more. The main batch of satellites were PocketQubes, which entered the atmosphere on May 2, 2022, by Rocket Lab Electron launched from New Zealand, with the additional purpose to test and validate the existing satellites in orbit. In October 2022, ACME received a US$150,000 Small Business Innovation Research (SBIR) grant from the National Oceanic and Atmospheric Administration (NOAA) to improve the app's wildfire detection and air quality measurement technology to better detect smoke, aerosols, fire hotspots using satellites and aerial drones. On August 18, 2023, phase two of the NOAA grant was approved, providing an additional US$650,000 to aid in the app's aforementioned goals by launching a pair of CubeSat satellites to provide high-definition infrared imagery. On September 8, 2023, ACME secured another US$1,200,000 in crowd funding to aid accomplishing the goals of the NOAA grant by expanding the app's workforce from 35 to 100 employees by the end of 2024. In January 2024, MyRadar partnered with Total Traffic and Weather Network (TTWN) to provide traffic data overlaid with its pre-existing weather graphics for users in the United States. The partnership allowed for the app to additionally become a tool for navigation. This officially became a feature days later on January 8, 2024, when the app was made compatible with Apple's CarPlay. On February 7, 2024, the Android equivalent Android Auto also gained the ability to display the app on car interfaces. In March 2024, the app launched a "meteorological wedding planning service" in the United States and Canada for prices between US$1,000 and US$5,000, in which users can request a personal meteorologist to provide an in-person meeting about the best dates for a wedding, and on-call local weather updates the day of. Scheduled for February 2025, four more satellites to help with the NOAA-sponsored wildfire detection are to be launched, and the first by ACME to have AI processing in the satellites themself and not computers on the ground, allowing for quicker transfer of information. == Features and general information == The app's primary function is to provide weather forecasting and prediction to users. The app includes toggleable options to track and send alerts to users for rain, wind patterns, earthquakes, tornadoes, tropical cyclones, wildfires, and more. In early 2020, a feature was added to track orbital objects such as the International Space Station. In May 2022, with the imagery improvement of HORIS, the app gained the secondary abilities to better monitor algae blooms, coral reefs, illegal fishing, and wildfires. In January and February 2024, the ability to display traffic flow and incident data in a feature called "RouteCast" was added, and can be displayed in video and 3D options via CarPlay and Android Auto for users with paying subscriptions. The app also provides annual tropical storm and tornado outlooks for their respective seasons, gathered through satellite and aerial drone data, as well as through on the ground storm chasers.
Spatial anti-aliasing
In digital signal processing, spatial anti-aliasing is a technique for minimizing the distortion artifacts (aliasing) when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications. Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as black-and-white noise. In signal acquisition and audio, anti-aliasing is often done using an analog anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analog-to-digital converter. In digital photography, optical anti-aliasing filters made of birefringent materials smooth the signal in the spatial optical domain. The anti-aliasing filter essentially blurs the image slightly in order to reduce the resolution to or below that achievable by the digital sensor (the larger the pixel pitch, the lower the achievable resolution at the sensor level). == Examples == In computer graphics, anti-aliasing improves the appearance of "jagged" polygon edges, or "jaggies", so they are smoothed out on the screen. However, it incurs a performance cost for the graphics card and uses more video memory. The level of anti-aliasing determines how smooth polygon edges are (and how much video memory it consumes). Near the top of an image with a receding checker-board pattern, the image is difficult to recognise and often not considered aesthetically pleasing. In contrast, when anti-aliased the checker-board near the top blends into grey, which is usually the desired effect when the resolution is insufficient to show the detail. Even near the bottom of the image, the edges appear much smoother in the anti-aliased image. Multiple methods exist, including the sinc filter, which is considered a better anti-aliasing algorithm. When magnified, it can be seen how anti-aliasing interpolates the brightness of the pixels at the boundaries to produce grey pixels since the space is occupied by both black and white tiles. These help make the sinc filter antialiased image appear much smoother than the original. In a simple diamond image, anti-aliasing blends the boundary pixels; this reduces the aesthetically jarring effect of the sharp, step-like boundaries that appear in the aliased graphic. Anti-aliasing is often applied in rendering text on a computer screen, to suggest smooth contours that better emulate the appearance of text produced by conventional ink-and-paper printing. Particularly with fonts displayed on typical LCD screens, it is common to use subpixel rendering techniques like ClearType. Sub-pixel rendering requires special colour-balanced anti-aliasing filters to turn what would be severe colour distortion into barely-noticeable colour fringes. Equivalent results can be had by making individual sub-pixels addressable as if they were full pixels, and supplying a hardware-based anti-aliasing filter as is done in the OLPC XO-1 laptop's display controller. Pixel geometry affects all of this, whether the anti-aliasing and sub-pixel addressing are done in software or hardware. == Simplest approach to anti-aliasing == The most basic approach to anti-aliasing a pixel is determining what percentage of the pixel is occupied by a given region in the vector graphic - in this case a pixel-sized square, possibly transposed over several pixels - and using that percentage as the colour. A Python program producing a basic plot of a single, white-on-black anti-aliased point using the method is as follows: This method is generally best suited for simple graphics, such as basic lines or curves, and applications that would otherwise have to convert absolute coordinates to pixel-constrained coordinates, such as 3D graphics. It is a fairly fast function, but it is relatively low-quality, and gets slower as the complexity of the shape increases. For purposes requiring very high-quality graphics or very complex vector shapes, this will probably not be the best approach. Note: The plot_antialiased_point routine above cannot blindly set the colour value to the percent calculated. It must add the new value to the existing value at that location up to a maximum of 1. Otherwise, the brightness of each pixel will be equal to the darkest value calculated in time for that location which produces a very bad result. For example, if one point sets a brightness level of 0.90 for a given pixel and another point calculated later barely touches that pixel and has a brightness of 0.05, the final value set for that pixel should be 0.95, not 0.05. For more sophisticated shapes, the algorithm may be generalized as rendering the shape to a pixel grid with higher resolution than the target display surface (usually a multiple that is a power of 2 to reduce distortion), then using bicubic interpolation to determine the average intensity of each real pixel on the display surface. == Signal processing approach to anti-aliasing == In this approach, the ideal image is regarded as a signal. The image displayed on the screen is taken as samples, at each (x,y) pixel position, of a filtered version of the signal. Ideally, one would understand how the human brain would process the original signal, and provide an on-screen image that will yield the most similar response by the brain. The most widely accepted analytic tool for such problems is the Fourier transform; this decomposes a signal into basis functions of different frequencies, known as frequency components, and gives us the amplitude of each frequency component in the signal. The waves are of the form: cos ( 2 j π x ) cos ( 2 k π y ) {\displaystyle \ \cos(2j\pi x)\cos(2k\pi y)} where j and k are arbitrary non-negative integers. There are also frequency components involving the sine functions in one or both dimensions, but for the purpose of this discussion, the cosine will suffice. The numbers j and k together are the frequency of the component: j is the frequency in the x direction, and k is the frequency in the y direction. The goal of an anti-aliasing filter is to greatly reduce frequencies above a certain limit, known as the Nyquist frequency, so that the signal will be accurately represented by its samples, or nearly so, in accordance with the sampling theorem; there are many different choices of detailed algorithm, with different filter transfer functions. Current knowledge of human visual perception is not sufficient, in general, to say what approach will look best. == Two dimensional considerations == The previous discussion assumes that the rectangular mesh sampling is the dominant part of the problem. The filter usually considered optimal is not rotationally symmetrical, as shown in this first figure; this is because the data is sampled on a square lattice, not using a continuous image. This sampling pattern is the justification for doing signal processing along each axis, as it is traditionally done on one dimensional data. Lanczos resampling is based on convolution of the data with a discrete representation of the sinc function. If the resolution is not limited by the rectangular sampling rate of either the source or target image, then one should ideally use rotationally symmetrical filter or interpolation functions, as though the data were a two dimensional function of continuous x and y. The sinc function of the radius has too long a tail to make a good filter (it is not even square-integrable). A more appropriate analog to the one-dimensional sinc is the two-dimensional Airy disc amplitude, the 2D Fourier transform of a circular region in 2D frequency space, as opposed to a square region. One might consider a Gaussian plus enough of its second derivative to flatten the top (in the frequency domain) or sharpen it up (in the spatial domain), as shown. Functions based on the Gaussian function are natural choices, because convolution with a Gaussian gives another Gaussian whether applied to x and y or to the radius. Similarly to wavelets, another of its properties is that it is halfway between being localized in the configuration (x and y) and in the spectral (j and k) representation. As an interpolation function, a Gaussian alone seems too spread out to preserve the maximum possible detail, and thus the second derivative is added. As an example, when printing a photographic negative with plentiful processing capability and on a printer with a hexagonal pattern, there is no reason to use sinc function interpolation. Such interpolation would treat diagonal lines differently from horizontal and vertical lines, which is like a weak form of aliasing. == Practical real-time anti-aliasing approximations == There are only a handful of primitives used at the lowest level in a real-time rend
Unfold (app)
Unfold is a mobile application that allows users to create social media content using a variety of templates and other tools. It was founded in 2018 by Alfonso Cobo and Andy McCune. It enables users to add photos, video, and text with a variety of tools. In 2019, Unfold was acquired by Squarespace. == History == In January 2017, Alfonso Cobo was studying at Parsons School of Design when he realized there was no software or app that could create a portfolio of his work on an iPad. Cobo created an app called Portfolio, a basic version of a portfolio layout app, and the first one to exist for iPad. He launched it in 2017. After launching the first version of Portfolio, Cobo realized the more popular market and use case was on mobile. Around that time, Instagram was launching Stories. As a result, Cobo pivoted the app away from portfolios and instead focused on an app to showcase one's stories. Cobo later contacted Andy McCune, founder of social media account Earth, to collaborate with Unfold. Unfold also partnered with various companies to create custom templates. These include Equinox, Tommy Hilfiger, NARS, Billboard Music Awards, and Product Red. Unfold also launched a collection of Product Red templates to help eliminate HIV/AIDS in several African countries. In 2019, Squarespace acquired Unfold. The Unfold app has been downloaded over 60 million times and has been used to create over 1 billion Instagram stories. == Features == With Unfold, users can utilize hundreds of templates to make social content for social media platforms such as Instagram, Snapchat, and Facebook. The free app offers users basic templates and standard fonts, filters, and stickers, and there are also premium templates available for a monthly subscription. With Unfold+ and Unfold Pro (previously Unfold for Brands), users can access premium templates and tools, as well as upload custom brand assets and fonts. In 2020, Unfold launched Bio Sites, which allows users to link to multiple sites and platforms.