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The knowledge assessment methodology (KAM) is "an interactive benchmarking tool created by the World Bank's Knowledge for Development Program to help countries identify the challenges and opportunities they face in making the transition to the knowledge-based economy." KAM does so by providing information on knowledge economy indicators for 146 countries. Its products include the Knowledge Economy Index and the Knowledge Index.
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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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The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN depends on Nvidia's CUDA software, GPUs, and Google's TensorFlow, or Meta AI's PyTorch, which supersedes TensorFlow as the official implementation library in later StyleGAN versions. The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. Nvidia introduced StyleGAN3, described as an "alias-free" version, on June 23, 2021, and made source available on October 12, 2021. == History == A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces. StyleGAN was able to run on Nvidia's commodity GPU processors. In February 2019, Uber engineer Phillip Wang used the software to create the website This Person Does Not Exist, which displayed a new face on each web page reload. Wang himself has expressed amazement, given that humans are evolved to specifically understand human faces, that nevertheless StyleGAN can competitively "pick apart all the relevant features (of human faces) and recompose them in a way that's coherent." In September 2019, a website called Generated Photos published 100,000 images as a collection of stock photos. The collection was made using a private dataset shot in a controlled environment with similar light and angles. Similarly, two faculty at the University of Washington's Information School used StyleGAN to create Which Face is Real?, which challenged visitors to differentiate between a fake and a real face side by side. The faculty stated the intention was to "educate the public" about the existence of this technology so they could be wary of it, "just like eventually most people were made aware that you can Photoshop an image". The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. In 2021, a third version was released, improving consistency between fine and coarse details in the generator. Dubbed "alias-free", this version was implemented with PyTorch. === Illicit use === In December 2019, Facebook took down a network of accounts with false identities, and mentioned that some of them had used profile pictures created with machine learning techniques. == Architecture == === Progressive GAN === Progressive GAN is a method for training GAN for large-scale image generation stably, by growing a GAN generator from small to large scale in a pyramidal fashion. Like SinGAN, it decomposes the generator as G = G 1 ∘ G 2 ∘ ⋯ ∘ G N {\displaystyle G=G_{1}\circ G_{2}\circ \cdots \circ G_{N}} , and the discriminator as D = D N ∘ D N − 1 ∘ ⋯ ∘ D 1 {\displaystyle D=D_{N}\circ D_{N-1}\circ \cdots \circ D_{1}} . During training, at first only G N , D N {\displaystyle G_{N},D_{N}} are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images. To avoid discontinuity between stages of the GAN game, each new layer is "blended in" (Figure 2 of the paper). For example, this is how the second stage GAN game starts: Just before, the GAN game consists of the pair G N , D N {\displaystyle G_{N},D_{N}} generating and discriminating 4x4 images. Just after, the GAN game consists of the pair ( ( 1 − α ) + α ⋅ G N − 1 ) ∘ u ∘ G N , D N ∘ d ∘ ( ( 1 − α ) + α ⋅ D N − 1 ) {\displaystyle ((1-\alpha )+\alpha \cdot G_{N-1})\circ u\circ G_{N},D_{N}\circ d\circ ((1-\alpha )+\alpha \cdot D_{N-1})} generating and discriminating 8x8 images. Here, the functions u , d {\displaystyle u,d} are image up- and down-sampling functions, and α {\displaystyle \alpha } is a blend-in factor (much like an alpha in image composing) that smoothly glides from 0 to 1. === StyleGAN === StyleGAN is designed as a combination of Progressive GAN with neural style transfer. The key architectural choice of StyleGAN-1 is a progressive growth mechanism, similar to Progressive GAN. Each generated image starts as a constant 4 × 4 × 512 {\displaystyle 4\times 4\times 512} array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance). At training time, usually only one style latent vector is used per image generated, but sometimes two ("mixing regularization") in order to encourage each style block to independently perform its stylization without expecting help from other style blocks (since they might receive an entirely different style latent vector). After training, multiple style latent vectors can be fed into each style block. Those fed to the lower layers control the large-scale styles, and those fed to the higher layers control the fine-detail styles. Style-mixing between two images x , x ′ {\displaystyle x,x'} can be performed as well. First, run a gradient descent to find z , z ′ {\displaystyle z,z'} such that G ( z ) ≈ x , G ( z ′ ) ≈ x ′ {\displaystyle G(z)\approx x,G(z')\approx x'} . This is called "projecting an image back to style latent space". Then, z {\displaystyle z} can be fed to the lower style blocks, and z ′ {\displaystyle z'} to the higher style blocks, to generate a composite image that has the large-scale style of x {\displaystyle x} , and the fine-detail style of x ′ {\displaystyle x'} . Multiple images can also be composed this way. === StyleGAN2 === StyleGAN2 improves upon StyleGAN in two ways. One, it applies the style latent vector to transform the convolution layer's weights instead, thus solving the "blob" problem. The "blob" problem roughly speaking is because using the style latent vector to normalize the generated image destroys useful information. Consequently, the generator learned to create a "distraction" by a large blob, which absorbs most of the effect of normalization (somewhat similar to using flares to distract a heat-seeking missile). Two, it uses residual connections, which helps it avoid the phenomenon where certain features are stuck at intervals of pixels. For example, the seam between two teeth may be stuck at pixels divisible by 32, because the generator learned to generate teeth during stage N-5, and consequently could only generate primitive teeth at that stage, before scaling up 5 times (thus intervals of 32). This was updated by the StyleGAN2-ADA ("ADA" stands for "adaptive"), which uses invertible data augmentation. It also tunes the amount of data augmentation applied by starting at zero, and gradually increasing it until an "overfitting heuristic" reaches a target level, thus the name "adaptive". === StyleGAN3 === StyleGAN3 improves upon StyleGAN2 by solving the "texture sticking" problem, which can be seen in the official videos. They analyzed the problem by the Nyquist–Shannon sampling theorem, and argued that the layers in the generator learned to exploit the high-frequency signal in the pixels they operate upon. To solve this, they proposed imposing strict lowpass filters between each generator's layers, so that the generator is forced to operate on the pixels in a way faithful to the continuous signals they represent, rather than operate on them as merely discrete signals. They further imposed rotational and translational invariance by using more signal filters. The resulting StyleGAN-3 is able to generate images that rotate and translate smoothly, and without texture sticking.
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SatuSehat (Indonesian for "one health"), formerly PeduliLindungi (roughly "care to protect"), is a national integrated health data exchange platform, jointly developed by the Indonesian Ministry of Communication and Information Technology (Kemenkominfo), in partnership with Committee for COVID-19 Response and National Economic Recovery (KPCPEN), Ministry of Health (Kemenkes), Ministry of State-Owned Enterprises (KemenBUMN), and Telkom Indonesia. The SatuSehat platform aims to facilitate data accessibility and service efficiency for health providers and the government, and assist the public as a tool to access their own electronic medical record data. This app was the official COVID-19 contact tracing app used for digital contact tracing in Indonesia, and originally known as TraceTogether but later changed because Singapore had its app using the same name. == Implementation == On 23 August 2021, Coordinating Minister for Maritime and Investments Affairs, Luhut Binsar Panjaitan, encouraged the government to make this app a mandatory requirement before using public transportations, such as train, bus, ferry, and plane. Furthermore, citizen must have installed the app before entering shopping malls, factories, and sport venues. Every person who have received at least a dose of vaccine will receive a vaccine card and vaccination certificate which can be downloaded from the app. In December 2022, with the revocation of PPKM (Community Activities Restrictions Enforcement) starting from 1 January 2023, Ministry of Health issued a statement that the usage of the app is not a governmental mandatory requirement as it used to be. === Transition into a citizen health app === On 7 September 2022, it was announced that the app would be modified to become a citizen health app, capitalising on the reach of the app and the existing work done around the app. On 28 February 2023, the authorities announced that the app was rebranded to SATUSEHAT Mobile (lit. 'OneHealth Mobile'), with existing users needing to update the PeduliLindungi app and re-synchronise their COVID-19 related health information. The re-branded app would eventually be an all-in-one health service and records retrieval app for Indonesians. == Controversy == It was reported that the app requires continuous access to the phone's files, media, and GPS, which quickly drains the battery. Allowing location access only during use or denying it altogether will render the app unusable. This stands in stark contrast to COVID-19 apps used in other countries that only utilize Bluetooth and do not require any additional permissions. In September 2021, stored personal data of at least 1.3 million Indonesian residents were leaked online, including the vaccine certificate of President Joko Widodo. The data leak was also reported on eHAC (electronic Health Alert Card), a mandatory app used for air passengers.
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Couch to 5K, abbreviated C25K, is an exercise plan that gradually progresses from beginner running toward a 5 kilometre (3.1 mile) run over nine weeks. == Operations == The Couch to 5K running plan, also known as C25K, created by Josh Clark in 1996, was developed with the expectation of creating a plan for new runners to start running. The plan is aimed to have users work out for 20 to 30 minutes, three days a week. Within the program, users can be expected to perform different tasks such as intervals of running with period of short walks in between to help build endurance in the weeks up to the final goal of a 5K run. During the nine weeks leading up to the race, the runner will learn to set their own pace and where their strengths and weaknesses are within running. Often, the daily workouts start with a five-minute warm-up walk and works up to running five kilometres without a walking break within nine weeks. Users are not expected to have any experience in running and can be some of the first running that they ever do. The main goal is to turn that unexperienced runner into someone who can run a 5K. Clark started the website Kick and featured C25K on the site. In 2001, Kick merged with Cool Running, a New England–based running site. Clark later sold his stake in Cool Running and the Couch to 5K program. Cool Running was absorbed into Active.com, operated by Active Network, LLC. Active Network provides mobile apps for Couch to 5K, as well as 5K to 10K, a follow-up program. The NHS in the UK provides downloadable podcasts and a smartphone app (Android and iOS) for the plan. A mobile app, created by Zen Labs, has training plans that are based on the Couch to 5K running plan from CoolRunning.com. It is one of the highest-rated health and fitness apps available on Android and iOS. As of 2016, the C25K app has been used by over 5 million people.
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The Common Image Generator Interface (CIGI) (pronounced sig-ee), is an on-the-wire data protocol that allows communication between an Image Generator and its host simulation. The interface is designed to promote a standard way for a host device to communicate with an image generator (IG) within the industry. CIGI enables plug-and-play by standard-compliant image generator vendors and reduces integration costs when upgrading visual systems. == Background == Most high-end simulators do not have everything running on a single machine the way popular home software flight simulators are currently implemented. The airplane model is run on one machine, normally referred to as the host, and the out the window visuals or scene graph program is run on another, usually referred to as an Image Generator (IG). Frequently there are multiple IGs required to display the surrounding environment created by a host. CIGI is the interface between the 'host' and the IGs. The main goal of CIGI is to capitalize on previous investments through the use of a common interface. CIGI is designed to assist suppliers and integrators of IG systems with ease of integration, code reuse, and overall cost reduction. In the past most image generators provided their own proprietary interface; every host had to implement that interface making changing image generators a costly ordeal. CIGI was created to standardize the interface between the host and the image generator so that little modification would be needed to switch image generators. The CIGI initiative was largely spearheaded by The Boeing Company during the early 21st century. The latest version of CIGI (CIGI 4.0) was developed by the Simulation Interoperability Standards Organization (SISO) in the form of SISO-STD-013-2014, Standard for Common Image Generator Interface (CIGI), Version 4.0, dated 22 August 2014. SISO-STD-013-2014 is freely available from SISO. == Definitions == Image generator – In this context an image generator consists of one or more rendering channels that produce an image that can be used to visualize an “Out-The-Window” scene, or images produced by various sensor simulations such as Infra-red, Day TV, electro-optical, and night vision. Host simulation – In this context a “Host” is the computational system that provides information about the device being simulated so that the image generator can portray the correct scenery to the user. This information is passed via CIGI to the image generator. == Maturation == CIGI 4 is the latest version of the standard as was approved by the Simulation Interoperability Standards Organization on August 22, 2014. CIGI became an international SISO standard known as SISO-STD-013-2014; which contains the CIGI version 4.0 Interface Control Document (ICD). CIGI 4.0 is the official standard, published by SISO. Previous versions of CIGI were spearheaded by Boeing include CIGI v3.3, in November 2008, v3.2 April 2006, v3.1 June 2004, v3 November 2003, v2 in March 2002, and the original (v1) in March 2001 == Protocol dependencies == Typically, CIGI uses UDP as its transport protocol, but CIGI does not require a specific transport mechanism, only packet definition conformance. CIGI traffic does not have a well known port; however, the use of ports 8004-8005 has been widely adopted by commercial image generator vendors implementations. == Development tools == === Host Emulator === The Host Emulator can be used as a surrogate to manipulate the interface when a simulation Host is not available. It is a Windows-based image generator Host application used to develop, integrate and test image generators that use the CIGI protocol. It provides a graphical user interface (GUI) for the creation, modification and deletion of entities; manipulation of views; control of environmental attributes and phenomena; and other host functions. The Host Emulator has several features that are useful for integration and testing. A free-flight mode allows for fixed-wing and rotorcraft flight, movement along entity axes and free rotation using a joystick or a joystick-like widget. Scripting and record/playback features support regression testing, demonstrations and other tasks needing exact reproduction of certain sequences of events. A packet-level snoop feature allows the user to examine the contents of CIGI messages, image generator response times and latencies. A Heartbeat Monitor Window shows a graphical timing history of the Image Generator's data frame rate. Other features include explicit packet creation, animation control, missile flyouts and a situation display window (Host Emulator 3.x only). === Multi-Purpose Viewer === The Multi-Purpose Viewer (MPV) provides the basic functionality expected of an Image Generator, such as loading and displaying a terrain database, displaying entities and so forth. The Multi-Purpose Viewer can be used as a surrogate to manipulate the interface when a real Image Generator is not available. The MPV is capable of operating with both the Windows and Linux operating systems. === CIGI Class Library === The CCL is an object-oriented software interface that automatically handles message composition and decomposition (i.e. packing, unpacking and byte swapping to the ICD specification) on both the Host and Image Generator sides of the interface. The CCL interprets Host or Image Generator messages based on compile time parameters. It also performs error handling and translation between different versions of CIGI. Each packet type has its own class. The individual packet members are accessed through packet class accessors. Outgoing messages are constructed by placing each packet into the outgoing buffer using a streaming operator. Incoming messages are parsed using callback or event-based mechanisms that supply the using program with fully populated packet objects. === Current tool suite === A set of CIGI development tools are managed and maintained by the SISO CIGI Product Support Group. The latest packages are available on SourceForge. Comments/Suggestions to the package can be directed to the SISO discussion board at: https://discussions.sisostds.org/index.htm?A0=SAC-PSG-CIGI Archived 2017-09-13 at the Wayback Machine === Wireshark === Wireshark is a free and open source packet analyzer. It is used for network troubleshooting, analysis, software and communications protocol development, and education. Wireshark provides a dissector for CIGI packets. As of October 2016, “The CIGI dissector is fully functional for CIGI version 2 and 3. Version 1 is not yet implemented.” === Older versions of CIGI === A CIGI Interface Control Document (ICD) and development suite is available in open source format. The tools, ICD, and accompanying user documentation can be found and downloaded from the CIGI sourceforge web site. The SourceForge version of the MPV is limited in its support of CIGI data packets and is intended to grow as needs arise. The MPV uses CIGI 3 as its interface, but the MPV is backward-compatible with earlier CIGI versions through the use of the CCL. The MPV uses the Open Scene Graph library to render a scene. The scene graph is manipulated according to the CIGI commands received from the Host via the CCL. The MPV itself is an application layer that consists of a small kernel leveraging heavily on a plug-in architecture for ease of maintainability and flexibility. An implementer can implement the interface from scratch, however a full suite of integration tools is available. These tools consist of three elements. The Host Emulator (HE), the Multi-Purpose Viewer (MPV), and the CIGI Class Library (CCL).
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Metadatabase is a database model for (1) metadata management, (2) global query of independent databases, and (3) distributed data processing. The word metadatabase is an addition to the dictionary. Originally, metadata was only a common term referring simply to "data about data", such as tags, keywords, and markup headers. However, in this technology, the concept of metadata is extended to also include such data and knowledge representation as information models (e.g., relations, entities-relationships, and objects), application logic (e.g., production rules), and analytic models (e.g., simulation, optimization, and mathematical algorithms). In the case of analytic models, it is also referred to as a Modelbase. These classes of metadata are integrated with some modeling ontology to give rise to a stable set of meta-relations (tables of metadata). Individual models are interpreted as metadata and entered into these tables. As such, models are inserted, retrieved, updated, and deleted in the same manner as ordinary data do in an ordinary (relational) database. Users will also formulate global queries and requests for processing of local databases through the Metadatabase, using the globally integrated metadata. The Metadatabase structure can be implemented in any open technology for relational databases. == Significance == The Metadatabase technology is developed at Rensselaer Polytechnic Institute at Troy, New York, by a group of faculty and students (see the references at the end of the article), starting in late 1980s. Its main contribution includes the extension of the concept of metadata and metadata management, and the original approach of designing a database for metadata applications. These conceptual results continue to motivate new research and new applications. At the level of particular design, its openness and scalability is tied to that of the particular ontology proposed: It requires reverse-representation of the application models in order to save them into the meta-relations. In theory, the ontology is neutral, and it has been proven in some industrial applications. However, it needs more development to establish it for the field as an open technology. The requirement of reverse-representation is common to any global information integration technology. A way to facilitate it in the Metadatabase approach is to distribute a core portion of it at each local site, to allow for peer-to-peer translation on the fly.
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SABSA (Sherwood Applied Business Security Architecture) is a model and methodology for developing a risk-driven enterprise information security architecture and service management, to support critical business processes. It was developed independently from the Zachman Framework, but has a similar structure. The primary characteristic of the SABSA model is that everything must be derived from an analysis of the business requirements for security, especially those in which security has an enabling function through which new business opportunities can be developed and exploited. The process analyzes the business requirements at the outset, and creates a chain of traceability through the strategy and concept, design, implementation, and ongoing ‘manage and measure’ phases of the lifecycle to ensure that the business mandate is preserved. Framework tools created from practical experience further support the whole methodology. The model is layered, with the top layer being the business requirements definition stage. At each lower layer a new level of abstraction and detail is developed, going through the definition of the conceptual architecture, logical services architecture, physical infrastructure architecture and finally at the lowest layer, the selection of technologies and products (component architecture). The SABSA model itself is generic and can be the starting point for any organization, but by going through the process of analysis and decision-making implied by its structure, it becomes specific to the enterprise, and is finally highly customized to a unique business model. It becomes in reality the enterprise security architecture, and it is central to the success of a strategic program of information security management within the organization. SABSA is a particular example of a methodology that can be used both for IT (information technology) and OT (operational technology) environments. == SABSA matrix == Note: The above is the original SABSA Matrix, which is still valid today, but it has been expanded by a comprehensive service management matrix and updated in some detail and terminology areas. In the words of David Lynas, SABSA author, "The SABSA Matrix and the SABSA Service Management Matrix have not been updated since the late 90s. We have redesigned them to deliver the improvements your feedback has requested over the years. We have not fundamentally changed the structure or principles of the matrices (very few elements have changed position) but have focused on terminology update and consistency." The new versions can be downloaded (along with the 2009 revision of the SABSA White Paper and other important documents like the SABSA Certification Roadmap) at the SABSA Members' Web Site.
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PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.
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Color digital images are made of pixels, and pixels are made of combinations of primary colors represented by a series of code. A channel in this context is the grayscale image of the same size as a color image, made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel. In geographic information systems, channels are often referred to as raster bands. Another closely related concept is feature maps, which are used in convolutional neural networks. == Overview == In the digital realm, there can be any number of conventional primary colors making up an image; a channel in this case is extended to be the grayscale image based on any such conventional primary color. By extension, a channel is any grayscale image of the same dimension as and associated with the original image. Channel is a conventional term used to refer to a certain component of an image. In reality, any image format can use any algorithm internally to store images. For instance, GIF images actually refer to the color in each pixel by an index number, which refers to a table where three color components are stored. However, regardless of how a specific format stores the images, discrete color channels can always be determined, as long as a final color image can be rendered. The concept of channels is extended beyond the visible spectrum in multispectral and hyperspectral imaging. In that context, each channel corresponds to a range of wavelengths and contains spectroscopic information. The channels can have multiple widths and ranges. Three main channel types (or color models) exist, and have respective strengths and weaknesses. === RGB images === An RGB image has three channels: red, green, and blue. RGB channels roughly follow the color receptors in the human eye, and are used in computer displays and image scanners. If the RGB image is 24-bit (the industry standard as of 2005), each channel has 8 bits, for red, green, and blue—in other words, the image is composed of three images (one for each channel), where each image can store discrete pixels with conventional brightness intensities between 0 and 255. If the RGB image is 48-bit (very high color-depth), each channel has 16-bit per pixel color, that is 16-bit red, green, and blue for each per pixel. ==== RGB color sample ==== Notice how the grey trees have similar brightness in all channels, the red dress is much brighter in the red channel than in the other two, and how the green part of the picture is shown much brighter in the green channel. === YUV === YUV images are an affine transformation of the RGB colorspace, originated in broadcasting. The Y channel correlates approximately with perceived intensity, whilst the U and V channels provide colour information. === CMYK === A CMYK image has four channels: cyan, magenta, yellow, and key (black). CMYK is the standard for print, where subtractive coloring is used. A 32-bit CMYK image (the industry standard as of 2005) is made of four 8-bit channels, one for cyan, one for magenta, one for yellow, and one for key color (typically is black). 64-bit storage for CMYK images (16-bit per channel) is not common, since CMYK is usually device-dependent, whereas RGB is the generic standard for device-independent storage. ==== CMYK color sample ==== === HSV === HSV, or hue saturation value, stores color information in three channels, just like RGB, but one channel is devoted to brightness (value), and the other two convey colour information. The value channel is similar to (but not exactly the same as) the CMYK black channel, or its negative. HSV is especially useful in lossy video compression, where loss of color information is less noticeable to the human eye. == Alpha channel == The alpha channel stores transparency information—the higher the value, the more opaque that pixel is. No camera or scanner measures transparency, although physical objects certainly can possess transparency, but the alpha channel is extremely useful for compositing digital images together. Bluescreen technology involves filming actors in front of a primary color background, then setting that color to transparent, and compositing it with a background. The GIF and PNG image formats use alpha channels on the World Wide Web to merge images on web pages so that they appear to have an arbitrary shape even on a non-uniform background. == Other channels == In 3D computer graphics, multiple channels are used for additional control over material rendering; e.g., controlling specularity and so on. == Bit depth == In digitizing images, the color channels are converted to numbers. Since images contain thousands of pixels, each with multiple channels, channels are usually encoded in as few bits as possible. Typical values are 8 bits per channel or 16 bits per channel. Indexed color effectively gets rid of channels altogether to get, for instance, 3 channels into 8 bits (GIF) or 16 bits. == Optimized channel sizes == Since the brain does not necessarily perceive distinctions in each channel to the same degree as in other channels, it is possible that differing the number of bits allocated to each channel will result in more optimal storage; in particular, for RGB images, compressing the blue channel the most and the red channel the least may be better than giving equal space to each. Among other techniques, lossy video compression uses chroma subsampling to reduce the bit depth in color channels (hue and saturation), while keeping all brightness information (value in HSV). 16-bit HiColor stores red and blue in 5 bits, and green in 6 bits.
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Security Controls for Computer Systems, commonly called the Ware report, is a 1970 text by Willis Ware that was foundational in the field of computer security. == Development == A defense contractor in St. Louis, Missouri, had bought an IBM mainframe computer, which it was using for classified work on a fighter aircraft. To provide additional income, the contractor asked the Department of Defense (DoD) for permission to sell computer time on the mainframe to local businesses via remote terminals, while the classified work continued. At the time, the DoD did not have a policy to cover this. The DoD's Advanced Research Projects Agency (DARPA) asked Ware - a RAND employee - to chair a committee to examine and report on the feasibility of security controls for computer systems. The committee's report was a classified document given in January 1970 to the Defense Science Board (DSB), which had taken over the project from ARPA. After declassification, the report was published by RAND in October 1979. == Influence == The IEEE Computer Society said the report was widely circulated, and the IEEE Annals of the History of Computing said that it, together with Ware's 1967 Spring Joint Computer Conference session, marked the start of the field of computer security. The report influenced security certification standards and processes, especially in the banking and defense industries, where the report was instrumental in creating the Orange Book.
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The Style Generative Adversarial Network, or StyleGAN for short, is an extension to the GAN architecture introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN depends on Nvidia's CUDA software, GPUs, and Google's TensorFlow, or Meta AI's PyTorch, which supersedes TensorFlow as the official implementation library in later StyleGAN versions. The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. Nvidia introduced StyleGAN3, described as an "alias-free" version, on June 23, 2021, and made source available on October 12, 2021. == History == A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces. StyleGAN was able to run on Nvidia's commodity GPU processors. In February 2019, Uber engineer Phillip Wang used the software to create the website This Person Does Not Exist, which displayed a new face on each web page reload. Wang himself has expressed amazement, given that humans are evolved to specifically understand human faces, that nevertheless StyleGAN can competitively "pick apart all the relevant features (of human faces) and recompose them in a way that's coherent." In September 2019, a website called Generated Photos published 100,000 images as a collection of stock photos. The collection was made using a private dataset shot in a controlled environment with similar light and angles. Similarly, two faculty at the University of Washington's Information School used StyleGAN to create Which Face is Real?, which challenged visitors to differentiate between a fake and a real face side by side. The faculty stated the intention was to "educate the public" about the existence of this technology so they could be wary of it, "just like eventually most people were made aware that you can Photoshop an image". The second version of StyleGAN, called StyleGAN2, was published on February 5, 2020. It removes some of the characteristic artifacts and improves the image quality. In 2021, a third version was released, improving consistency between fine and coarse details in the generator. Dubbed "alias-free", this version was implemented with PyTorch. === Illicit use === In December 2019, Facebook took down a network of accounts with false identities, and mentioned that some of them had used profile pictures created with machine learning techniques. == Architecture == === Progressive GAN === Progressive GAN is a method for training GAN for large-scale image generation stably, by growing a GAN generator from small to large scale in a pyramidal fashion. Like SinGAN, it decomposes the generator as G = G 1 ∘ G 2 ∘ ⋯ ∘ G N {\displaystyle G=G_{1}\circ G_{2}\circ \cdots \circ G_{N}} , and the discriminator as D = D N ∘ D N − 1 ∘ ⋯ ∘ D 1 {\displaystyle D=D_{N}\circ D_{N-1}\circ \cdots \circ D_{1}} . During training, at first only G N , D N {\displaystyle G_{N},D_{N}} are used in a GAN game to generate 4x4 images. Then G N − 1 , D N − 1 {\displaystyle G_{N-1},D_{N-1}} are added to reach the second stage of GAN game, to generate 8x8 images, and so on, until we reach a GAN game to generate 1024x1024 images. To avoid discontinuity between stages of the GAN game, each new layer is "blended in" (Figure 2 of the paper). For example, this is how the second stage GAN game starts: Just before, the GAN game consists of the pair G N , D N {\displaystyle G_{N},D_{N}} generating and discriminating 4x4 images. Just after, the GAN game consists of the pair ( ( 1 − α ) + α ⋅ G N − 1 ) ∘ u ∘ G N , D N ∘ d ∘ ( ( 1 − α ) + α ⋅ D N − 1 ) {\displaystyle ((1-\alpha )+\alpha \cdot G_{N-1})\circ u\circ G_{N},D_{N}\circ d\circ ((1-\alpha )+\alpha \cdot D_{N-1})} generating and discriminating 8x8 images. Here, the functions u , d {\displaystyle u,d} are image up- and down-sampling functions, and α {\displaystyle \alpha } is a blend-in factor (much like an alpha in image composing) that smoothly glides from 0 to 1. === StyleGAN === StyleGAN is designed as a combination of Progressive GAN with neural style transfer. The key architectural choice of StyleGAN-1 is a progressive growth mechanism, similar to Progressive GAN. Each generated image starts as a constant 4 × 4 × 512 {\displaystyle 4\times 4\times 512} array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance). At training time, usually only one style latent vector is used per image generated, but sometimes two ("mixing regularization") in order to encourage each style block to independently perform its stylization without expecting help from other style blocks (since they might receive an entirely different style latent vector). After training, multiple style latent vectors can be fed into each style block. Those fed to the lower layers control the large-scale styles, and those fed to the higher layers control the fine-detail styles. Style-mixing between two images x , x ′ {\displaystyle x,x'} can be performed as well. First, run a gradient descent to find z , z ′ {\displaystyle z,z'} such that G ( z ) ≈ x , G ( z ′ ) ≈ x ′ {\displaystyle G(z)\approx x,G(z')\approx x'} . This is called "projecting an image back to style latent space". Then, z {\displaystyle z} can be fed to the lower style blocks, and z ′ {\displaystyle z'} to the higher style blocks, to generate a composite image that has the large-scale style of x {\displaystyle x} , and the fine-detail style of x ′ {\displaystyle x'} . Multiple images can also be composed this way. === StyleGAN2 === StyleGAN2 improves upon StyleGAN in two ways. One, it applies the style latent vector to transform the convolution layer's weights instead, thus solving the "blob" problem. The "blob" problem roughly speaking is because using the style latent vector to normalize the generated image destroys useful information. Consequently, the generator learned to create a "distraction" by a large blob, which absorbs most of the effect of normalization (somewhat similar to using flares to distract a heat-seeking missile). Two, it uses residual connections, which helps it avoid the phenomenon where certain features are stuck at intervals of pixels. For example, the seam between two teeth may be stuck at pixels divisible by 32, because the generator learned to generate teeth during stage N-5, and consequently could only generate primitive teeth at that stage, before scaling up 5 times (thus intervals of 32). This was updated by the StyleGAN2-ADA ("ADA" stands for "adaptive"), which uses invertible data augmentation. It also tunes the amount of data augmentation applied by starting at zero, and gradually increasing it until an "overfitting heuristic" reaches a target level, thus the name "adaptive". === StyleGAN3 === StyleGAN3 improves upon StyleGAN2 by solving the "texture sticking" problem, which can be seen in the official videos. They analyzed the problem by the Nyquist–Shannon sampling theorem, and argued that the layers in the generator learned to exploit the high-frequency signal in the pixels they operate upon. To solve this, they proposed imposing strict lowpass filters between each generator's layers, so that the generator is forced to operate on the pixels in a way faithful to the continuous signals they represent, rather than operate on them as merely discrete signals. They further imposed rotational and translational invariance by using more signal filters. The resulting StyleGAN-3 is able to generate images that rotate and translate smoothly, and without texture sticking.
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Rake is a software task management and a build automation tool created by Jim Weirich. It allows the user to specify tasks and to describe dependencies as well as to group tasks into namespaces. It is similar to SCons and Make. Rake was written in Ruby and has been part of the standard library of Ruby since version 1.9. == Examples == The tasks that should be executed need to be defined in a configuration file called Rakefile. A Rakefile has no special syntax and contains executable Ruby code. === Tasks === The basic unit in Rake is the task. A task has a name and an action block, that defines its functionality. The following code defines a task called greet that will output the text "Hello, Rake!" to the console. When defining a task, you can optionally add dependencies, that is one task can depend on the successful completion of another task. Calling the "seed" task from the following example will first execute the "migrate" task and only then proceed with the execution of the "seed" task.Tasks can also be made more versatile by accepting arguments. For example, the "generate_report" task will take a date as argument. If no argument is supplied the current date is used.A special type of task is the file task, which can be used to specify file creation tasks. The following task, for example, is given two object files, i.e. "a.o" and "b.o", to create an executable program.Another useful tool is the directory convenience method, that can be used to create directories upon demand. === Rules === When a file is named as a prerequisite but it does not have a file task defined for it, Rake will attempt to synthesize a task by looking at a list of rules supplied in the Rakefile. For example, suppose we were trying to invoke task "mycode.o" with no tasks defined for it. If the Rakefile has a rule that looks like this: This rule will synthesize any task that ends in ".o". It has as a prerequisite that a source file with an extension of ".c" must exist. If Rake is able to find a file named "mycode.c", it will automatically create a task that builds "mycode.o" from "mycode.c". If the file "mycode.c" does not exist, Rake will attempt to recursively synthesize a rule for it. When a task is synthesized from a rule, the source attribute of the task is set to the matching source file. This allows users to write rules with actions that reference the source file. === Advanced rules === Any regular expression may be used as the rule pattern. Additionally, a proc may be used to calculate the name of the source file. This allows for complex patterns and sources. The following rule is equivalent to the example above: NOTE: Because of a quirk in Ruby syntax, parentheses are required around a rule when the first argument is a regular expression. The following rule might be used for Java files: === Namespaces === To better organize big Rakefiles, tasks can be grouped into namespaces. Below is an example of a simple Rake recipe:
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Overcast is a podcast app for iOS that was launched in 2014 by founder and operator Marco Arment. == Founder and operator == Arment was also the Chief Technology Officer of Tumblr and founder of Instapaper before founding Overcast, and he had created his own podcasts before launching the app. In March 2023, Arment told The Vergecast how he built and maintains Overcast by himself, and that he uses ad banners promoting podcasts to cover the costs of the free app. == Features and reception == In 2014, Overcast received positive reviews from MacWorld and iMore. In 2015, The Verge and The Sweet Setup each named it the best podcast app for iOS that year. In 2017, Discover Pods gave an endorsement citing the "smart speed" feature, which shortens quiet gaps in a podcast. In April 2019, Overcast introduced a feature that allowed users to share clips from podcasts to social media. In January 2020, Overcast was updated to allow users to skip the intros and outros of podcasts.
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Gooch shading is a non-photorealistic rendering technique for shading objects. It is also known as "cool to warm" shading, and is widely used in technical illustration. == History == Gooch shading was developed by Amy Gooch et al. at the University of Utah School of Computing and first presented at the 1998 SIGGRAPH conference. It has since been implemented in shader libraries, software, and games released by Autodesk, Nvidia, and Valve. == Process == Gooch shading defines an additional two colors in conjunction with the original model color: a warm color (such as yellow) and a cool color (such as blue). The warm color indicates surfaces that are facing toward the light source while the cool color indicates surfaces facing away. This allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. The Gooch shader is typically implemented in two passes: all objects in the scene are first drawn with the "cool to warm" shading, and in the second pass the object's edges are rendered in black.
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