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AI Art News — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Productivity software

Productivity software (also called personal productivity software or office productivity software) is application software used for producing information (such as documents, presentations, worksheets, databases, charts, graphs, digital paintings, electronic music and digital video). Its names arose from it increasing productivity, especially of individual office workers, from typists to knowledge workers, although its scope is now wider than that. Office suites, which brought word processing, spreadsheet, and relational database programs to the desktop in the 1980s, are the core example of productivity software. They revolutionized the office with the magnitude of the productivity increase they brought as compared with the pre-1980s office environments of typewriters, paper filing, and handwritten lists and ledgers. In the United States, as of 2015, some 78% of "middle-skill" occupations (those that call for more than a high school diploma but less than a bachelor's degree) required the use of productivity software. == Details == Productivity software traditionally runs directly on a computer. For example, Plus/4 model of computer contains in ROM for applications of productivity software. Productivity software is one of the reasons people use personal computers. == Office suite == An office suite is a bundle of productivity software (a software suite) intended to be used by office workers. The components are generally distributed together, have a consistent user interface and usually can interact with each other, sometimes in ways that the operating system would not normally allow. The earliest office suite for personal computers was MicroPro International's StarBurst in the early 1980s, comprising the WordStar word processor, the CalcStar spreadsheet and the DataStar database software. Other suites arose in the 1980s, and Microsoft Office came to dominate the market in the 1990s, a position it retains as of 2024. During the 1990s, office suite products gained popularity by offering bundles of applications that, when bought as part of a suite, effectively discounted the individual applications, with four or five applications being bundled for the price of two applications bought separately. When faced with such potential savings, customers could be "tempted by the suite, rather than the value of a particular product", and by 1994 more than 60 percent of the sales of Microsoft Word and around 70 percent of the sales of Microsoft Excel were as part of sales of Microsoft Office. Such considerations had an impact on vendors of individual applications, often smaller companies, raising concerns that office suites were "stifling innovation", and even established vendors such as Borland and WordPerfect were having to adapt to the suite phenomenon, Borland ultimately deciding to sell its Quattro Pro spreadsheet to WordPerfect as the latter sought to assemble its own suite product. The dominant suite vendors, Microsoft and Lotus, downplayed competition and innovation concerns, claiming that users were still able to exercise choice and that "user-driven development" was guiding the evolution of office suites. Another view was that component-based software would eventually emerge, focusing development on more specialised components used by productivity software, empowering "a plethora of third-party developers", and that a "mix and match" approach of such components would adapt to the user's way of working. === Office suite components === The base components of office suites are: Word processor Spreadsheet Presentation program Other components include: Database software Graphics suite (raster graphics editor, vector graphics editor, image viewer) Desktop publishing software Formula editor Diagramming software Email client Communication software Personal information manager Notetaking Groupware Project management software Table (information) Web log analysis software
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SAP StreamWork

SAP StreamWork is an enterprise collaboration tool from SAP SE released in March 2010, and discontinued in December 2015. StreamWork allowed real-time collaboration like Google Wave, but focused on business activities such as analyzing data, planning meetings, and making decisions. It incorporated technology from Box.net and Evernote to allow users to connect to online files and documents, and document-reader technology from Scribd allowed users to view documents directly within its environment. StreamWork supported the OpenSocial set of application programming interfaces (APIs), allowing it to connect to tools built by third-party developers, such as Google Docs. A version of StreamWork intended for large enterprises used a virtual appliance based on Novell's SUSE Linux Enterprise to connect it to business systems, including those from SAP.
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Pocket (service)

Pocket, formerly known as Read It Later, was a social bookmarking service for storing, sharing and discovering web bookmarks, first released in 2007. Mozilla, the developer of Pocket, announced in May 2025 that it was discontinuing the service and would shut it down in July of that year. == History == Pocket was introduced in August 2007 as a Mozilla Firefox browser extension named Read It Later by Nathan (Nate) Weiner. Once his product was used by millions of people, he moved his office to Silicon Valley and four other people joined the Read It Later team. Weiner's intention was for the application to be like a TiVo directory for web content and to give users access to that content on any device. Read It Later obtained venture capital investments of US$2.5 million in 2011 and $5.0 million in 2012. The 2011 funding came from Foundation Capital, Baseline Ventures, Google Ventures, Founder Collective and unnamed angel investors. The company rejected an acquisition offer by Evernote after showing concerns that Evernote intended to shut down the Read It Later service and amalgamate its functionality into Evernote's main service. Initially, the Read It Later app was available in a free version and a paid version that included additional features. After the rebranding to Pocket, all paid features were made available in a free and advertisement-free app. In May 2014, a paid subscription service called Pocket Premium was introduced, adding server-side storage of articles and more powerful search tools. In June 2015, Pocket was included in Firefox, via a toolbar button and link to a user's Pocket list in the bookmark's menu. The integration was controversial, as users displayed concerns for the direct integration of a proprietary service into an open source application, and that it could not be completely disabled without editing advanced settings, unlike other third-party extensions. A Mozilla spokesperson stated that the feature was meant to leverage the service's popularity among Firefox users and clarified that all code related to the integration was open source. The spokesperson added that "[Mozilla had] gotten lots of positive feedback about the integration from users". On February 27, 2017, Pocket announced that it had been acquired by Mozilla Corporation, the commercial arm of Firefox's non-profit development group. Mozilla staff stated that Pocket would continue to operate as an independent subsidiary but that it would be leveraged as part of an ongoing "Context Graph" project. There were plans to open-source the server-side code of Pocket, though only parts of the project had been open-sourced as of 2024. On May 22, 2025, Mozilla announced that it would shut down Pocket on July 8, 2025. Exports of user data would be available until October 8, 2025, when accounts would be deleted. The email newsletter Pocket Hits was rebranded as Ten Tabs on June 12 as part of the closure, with it being changed to release only on weekdays. == Functions == The application allows the user to save an article or web page to remote servers for later reading. The article is sent to the user's Pocket list (synced to all of their devices) for offline reading. Pocket makes the article more readable by removing clutter and enabling the user to add tags and adjust text settings. == User base == The application had 17 million users and 1 billion saves, as of September 2015. Pocket was listed among Time magazine's 50 Best Android Applications for 2013. == Reception == Kent German of CNET said that "Read It Later is oh so incredibly useful for saving all the articles and news stories I find while commuting or waiting in line." Erez Zukerman of PC World said that supporting the developer is enough reason to buy what he deemed a "handy app". Bill Barol of Forbes said that although Read It Later works less well than Instapaper, "it makes my beloved Instapaper look and feel a little stodgy." In 2015, Pocket was awarded a Material Design Award for Adaptive Layout by Google for their Android application.
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Cloud-based quantum computing

Cloud-based quantum computing refers to the remote access of quantum computing resources—such as quantum emulators, simulators, or processors—via the internet. Cloud access enables users to develop, test, and execute quantum algorithms without the need for direct interaction with specialized hardware, facilitating broader participation in quantum software development and experimentation. In 2016, IBM launched the IBM Quantum Experience, one of the first publicly accessible quantum processors connected to the cloud. In early 2017, researchers at Rigetti Computing demonstrated programmable quantum cloud access through their software platform Forest, which included the pyQuil Python library. Since the early-2020s, cloud-based quantum computing has grown significantly, with multiple providers offering access to a variety of quantum hardware modalities, including superconducting qubits, trapped ions, neutral atoms, and photonic systems. Major platforms such as Amazon Braket, Azure Quantum, and qBraid aggregate quantum devices from hardware developers like IonQ, Rigetti Computing, QuEra, Pasqal, Oxford Quantum Circuits, and IBM Quantum. These platforms provide unified interfaces for users to write and execute quantum algorithms across diverse backends, often supporting open-source SDKs such as Qiskit, Cirq, and PennyLane. The proliferation of cloud-based access has played a key role in accelerating quantum education, algorithm research, and early-stage application development by lowering the barrier to experimentation with real quantum hardware. Cloud-based quantum computing has expanded access to quantum hardware and tools beyond traditional research laboratories. These platforms support educational initiatives, algorithm development, and early-stage commercial applications. == Applications == Cloud-based quantum computing is used across education, research, and software development, offering remote access to quantum systems without the need for on-site infrastructure. === Education === Quantum cloud platforms have become valuable tools in education, allowing students and instructors to engage with real quantum processors through user-friendly interfaces. Educators use these platforms to teach foundational concepts in quantum mechanics and quantum computing, as well as to demonstrate and implement quantum algorithms in a classroom or laboratory setting. === Scientific Research === Cloud-based access to quantum hardware has enabled researchers to conduct experiments in quantum information, test quantum algorithms, and compare quantum hardware platforms. Experiments such as testing Bell's theorem or evaluating quantum teleportation protocols have been performed on publicly available quantum processors. === Software Development and Prototyping === Developers use cloud-based platforms to prototype quantum software applications across fields such as optimization, machine learning, and chemistry. These platforms offer SDKs and APIs that integrate classical and quantum workflows, enabling experimentation with quantum algorithms in real-world or simulated environments. === Public Engagement and Games === Quantum cloud tools have also been used to create educational games and interactive applications aimed at increasing public understanding of quantum concepts. These efforts help bridge the gap between theoretical content and intuitive learning. == Existing platforms == qBraid Lab by qBraid is a cloud-based platform for quantum computing. It provides software tools for researchers and developers in quantum, as well as access to quantum hardware. qBraid provides cloud based access to Microsoft Azure Quantum and Amazon Braket devices including IQM, QuEra, Pasqal, Rigetti, IonQ, QIR simulators, Amazon Braket simulators, and the NEC Vector Annealer, as of August 2025. qBraid's base version is free, where unlimited hardware and simulator access is available with the purchase of credits. Quandela Cloud by Quandela is the platform to access first cloud-accessible European photonic quantum computer. The computer is interfaced using the Perceval scripting language, with tutorials and documentation available online for free. Xanadu Quantum Cloud by Xanadu is a platform with cloud-based access to three fully programmable photonic quantum computers. Forest by Rigetti Computing is a tool suite for cloud-based quantum computing. It includes a programming language, development tools and example algorithms. LIQUi> by Microsoft is a software architecture and tool suite for quantum computing. It includes a programming language, example optimization and scheduling algorithms, and quantum simulators. Q#, a quantum programming language by Microsoft on the .NET Framework seen as a successor to LIQUi|>. IBM Quantum Platform by IBM, providing access to quantum hardware as well as HPC simulators. These can be accessed programmatically using the Python-based Qiskit framework, or via graphical interface with the IBM Q Experience GUI. Both are based on the OpenQASM standard for representing quantum operations. There is also a tutorial and online community. Quantum in the Cloud by The University of Bristol, which consists of a quantum simulator and a four qubit optical quantum system. Quantum Playground by Google is an educational resource which features a simulator with a simple interface, and a scripting language and 3D quantum state visualization. Quantum in the Cloud is an experimental quantum cloud platform for access to a four-qubit nuclear magnetic resonance-NMRCloudQ computer, managed by Tsinghua University. Quantum Inspire by Qutech is the first platform in Europe providing cloud-based quantum computing to two hardware chips. Next to a 5-qubit transmon processor, Quantum Inspire is the first platform in the world to provide online access to a fully programmable 2-qubit electron spin quantum processor. Amazon Braket is a cloud-based quantum computing platform hosted by AWS which, as of June 2025, provides access to quantum computers built by IonQ, Rigetti, IQM, and QuEra. Braket also provides a quantum algorithm development environment and simulator. Forge by QC Ware is a cloud-based quantum computing platform that provides access to D-Wave hardware, as well as Google and IBM simulators. The platform offers a 30-day free trial, including one minute of quantum computing time. Quantum-as-a-Service by Scaleway is a cloud-based platform created in 2022 to access to real quantum hardware from IQM Quantum Computers, Alpine Quantum Technologies, Quandela and Pasqal. It also include access to GPU-powered emulators such as Aer, Qsim and Quandela proprietary emulation.
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Clean Email

Clean Email is an automated software as a service email management application which identifies and clears junk mail from inboxes. The service uses a subscription business model with a free trial for the first 1,000 emails. and is available on macOS, iOS, Android, and on the web. == History == Clean Email is a self-funded company headquartered in Los Angeles, California. Initially developed by the founder for personal use, the service was designed to address the growing issue of inbox clutter and privacy concerns. In 2017, John Gruber recognized Clean Email as a trustworthy alternative to Unroll.me after the latter was found to be selling user data. == Features == Clean Email uses algorithms to identify and categorize emails, enabling users to group, remove, label, and archive email messages in bulk. Its Unsubscriber tool consolidates all subscriptions and newsletters into a single view for quick management, allowing users to bulk unsubscribe or temporarily pause mail. Its Screener feature transforms the inbox into an "opt-in" system, enabling users to pre-approve mail from new senders. Cleaning Suggestions identifies frequently cleaned mail, recommending actions accordingly. Additional functionalities include automatic deletion of aging emails, delivery of messages to specified folders, and options to mute or block senders.
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Kubity

Kubity is a cloud-based 3D communication tool that works on desktop computers, the web, smartphones, tablets, augmented reality gear, and virtual reality glasses. Kubity is powered by several proprietary 3D processing engines including "Paragone" and "Etna" that prepare the 3D file for transfer over mobile devices. Kubity has practical applications for architecture, interior design, engineering, product design, film, and video games among others. The majority of its users create 3D models using SketchUp or Autodesk Revit software. Kubity products include the Kubity web app and Kubity Go (a mobile application for iOS and Android). Kubity is compatible across many platforms, devices and operating systems including: iOS, Android, Firefox, Chrome, Windows, MacOS, and Linux. == History == Kubity was created by SPK Technology (ex Kubity S.A.S.), a Paris-based software company specializing in automatic 3D data optimization and visualization. Founded in 2012 by a group of software engineers and an urban projects developer, they united around a simple idea: create a way for anyone, anywhere to simply and intuitively explore 3D models on smartphones and computers. In order to bring architects, engineers and designers together with their clients around a 3D model, it was essential to develop an interactive platform that supported multiple desktop and mobile devices for instantaneous and fluid 3D navigation. With specifications in place, 15 engineers fused together several technologies: 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and virtual reality headset integration. In January 2014, the first public Kubity prototype (1.0 Amethyst) was launched to a small group of beta testers with a plug-in that allowed users to import 3D models from SketchUp to their browser. A global release was announced in April 2014 at the SketchUp Basecamp in Vail, Colorado. In May 2015, Kubity launched a web application that worked using WebGL technology (2.0 Citrine). For the first time, users were able to drag and drop any SketchUp file in a web browser without having to install a plug-in. In December 2015, Kubity launched a mobile application on the App Store for iPhone, iPod, and iPad as well as on Google Play for Android devices (3.0 Druzy). In November 2016, Kubity launched support for Oculus Rift and HTC Vive (4.0 Emerald). Beginning in November 2017, Kubity launched a full suite rollout of mobile applications over six months that included Kubity AR for augmented reality, Kubity VR for virtual reality, and Kubity Mirror for remote presentations and screen mirroring (5.0 Feldspar). In September 2018, a one-click plugin for SketchUp and Revit (Kubity PRO), along with a mobile-first revamp of Kubity Go was launched, allowing PRO-to-Go device pairing for automatic mobile sync (6.0 Gypsum). In early 2019, the Kubity Go application was updated to include fully integrated AR, VR, and screen mirroring functionalities, killing off the dedicated companion apps Kubity AR, Kubity VR and Kubity Mirror in the process (7.0 Heliotrope). In January 2020, support for the Kubity PRO plugin for SketchUp and Revit was migrated to a SketchUp-only web app. == Technology == Kubity is powered by a proprietary 3D crystallization engine known as "Paragone"; a technology developed by SPK Technology. Paragone takes constrained information from a 3D file and runs it through the "BlockWave" algorithm (US Patent 10,482.629), also developed by SPK Technology. BlockWave is a multiphase optimization algorithm that combines 3D design, data compression, decimation and rendering optimization, web and mobile transfer, and mixed reality headset integration to create a crystallized universal format of the original file. One phase of the BlockWave algorithm is based on the quadric-based polygonal surface simplification algorithm, performed using predefined heuristics, and is associated with a plurality of simplified versions of the 3D model, each version being associated with a predefined level of detail adapted to the user specific end device. BlockWave extracts data content, geometry and textures, then sets quadrics for each top of the original 3D model, and identifies pairs of adjacent tops linked by vertices. The algorithm uses a local collapsing operator and a top-plan error metric to obtain a fixed number of faces or a maximum defined error; 3D meshing is simplified by replacing two points with one, then deleting the degrading faces and updating adjacent relations. Once decimation is completed, texture optimization is set using texture target parameters allowing maximized GPU memory to improve computing time. With texture encoding completed, the crystallized universal 3D file can now be easily opened on any user-specific end device and played across most digital devices with real-time rendering. == Features == === 3D Crystallization === A user converts (or crystallizes) a 3D file by exporting it with the Kubity web app. Crystallization adds features like AR/VR and cinematic fly-through tour as well as assigns the model a dedicated QR code. === Automatic Mobile Sync === When a 3D model is exported, it is automatically synced to Kubity Go on the user's mobile device. From there, it can be accessed, explored, and shared with others with or without an internet connection. === Security and Management === User models can be managed all in one place on Kubity Go or in a browser from their account. Models can be renamed, password-protected, shared, and played. === Augmented Reality === Developed using Apple ARKit and Google ARCore technology, Kubity Go's augmented reality feature maps the environment in a room detecting horizontal planes like tables and floors to track and place 3D objects. By blending digital objects and information with the environment, Kubity allows users to interact with 3D models in true augmented reality. Built-in communication features allows users to instantly share 3D models with anyone over text, email, social media, or direct device-to-device with a QR Code. Platform Support AR supports devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017). AR for Android requires Android 7.0 or later and access to the Google Play Store. === Virtual Reality === VR allows users to explore SketchUp models and Revit projects on-the-go right from a mobile device using Oculus Go, Google Cardboard, Samsung Gear VR, or the glasses-free Magic Window feature. Kubity's virtual reality feature is compatible with Oculus Go, Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the mobile virtual reality headset, Samsung Gear VR. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018), and Samsung Galaxy S9/Galaxy S9+. === Screen Mirroring === Screen mirroring allows a user to sync the sender device to a receiver on a webpage, then control from the sender device to give a remote presentation of the 3D model. Devices are easily synced by entering a six-digit number displayed on the receiving computer. == Platform support == On iOS, the Kubity application is compatible with devices running on the version 9.0 or higher. On Android, Kubity is compatible with devices running on the version 4.4 “Kit Kat” or higher. The web version of Kubity applications currently support web browsers compatible with WebGL2 : Mozilla Firefox and Google Chrome. AR is compatible with devices running iOS11 including: iPhone SE, iPhone 6s, iPhone 6s Plus, iPhone 7, iPhone 7 Plus, iPhone 8, iPhone X, all iPad Pro models, and iPad (2017), and Android devices. Requires Android 7.0 or later and access to the Google Play Store. VR is compatible with Google Cardboard viewers and other cardboard compatible devices including clip-on style VR glasses like Homido Mini, as well as the Samsung Gear VR and Oculus Go. Samsung Gear VR supports: Galaxy S6, Galaxy S6 Edge, Galaxy S6 Edge+, Samsung Galaxy Note 5, Galaxy S7, Galaxy S7 Edge, Galaxy S8, Galaxy S8+, Samsung Galaxy Note Fan Edition, Samsung Galaxy Note 8, Samsung Galaxy A8/A8+ (2018) and Samsung Galaxy S9/Galaxy S9+.
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Curve (tonality)

In image editing, a curve is a remapping of image tonality, specified as a function from input level to output level, used as a way to emphasize colours or other elements in a picture. Curves can usually be applied to all channels together in an image, or to each channel individually. Applying a curve to all channels typically changes the brightness in part of the spectrum. Light parts of a picture can be easily made lighter and dark parts darker to increase contrast. Applying a curve to individual channels can be used to stress a colour. This is particularly efficient in the Lab colour space due to the separation of luminance and chromaticity, but it can also be used in RGB, CMYK or whatever other colour models the software supports.
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Azure Maps

Azure Maps is a suite of cloud-based, location-based services provided by Microsoft as part of the company's Azure platform. The platform provides geospatial and location-based services via REST APIs and software development kits (SDKs). The service is typically used to integrate maps or geospatial data into applications. Azure Maps differs from Microsoft's other enterprise mapping service, Bing Maps, in its pricing model, focus on privacy, and its level of integration into the broader Azure cloud ecosystem. == History == Azure Maps was first introduced in public preview mode under the name "Azure Location Based Services" in 2017, primarily as an enterprise solution. The services was intended to add mapping and location-based functionality onto the existing Azure cloud services suite, seen as a critical part of Microsoft's broader Internet-of-Things (IoT) strategy. The preview version included APIs which could be used to develop location aware apps for use cases such as logistics and mobility. In 2018, the software was renamed "Azure Maps," and became generally available to the public, and a number of new functions were added, including route calculation, travel time calculation, and incorporation of real-time traffic data and incident information. Azure Maps was integrated with Azure IoT Central in 2018, which added tracking, monitoring, and geofencing capabilities. A set of mobility APIs on were added in 2019, with applications such as use in public transport apps and shared bicycle fleet management. “Azure Maps Creator,” which converts private facility floor plans into indoor map data, was also introduced in 2019. Some commentators linked these services to Microsoft's broader development of augmented reality products. In 2020, Azure Maps Visual for Power BI was released, integrating location-based features and mapping capabilities into Microsoft's business intelligence software. An elevation API (which was later retired), geolocation services, and an iOS and Android software development kit were introduced in 2021. In 2022, support for historical weather, air quality, and tropical storm data was made generally available and custom styling for indoor maps was also introduced. In 2023, Azure Maps was certified as HIPAA compliant in a move to target healthcare and health insurance companies. == Functionality == === Geocoding === Geocoding is one of the core functionalities of Azure Maps, converting addresses or place names into geographic coordinates. Batch geocoding is used to process large amounts of address data, a function used for route optimization and spatial analysis. === Reverse geocoding === Reverse geocoding derives human-readable information from geographic coordinates like longitude and latitude, used in navigation and by geographic information systems. === Routing === Azure Maps uses map data and routing algorithms to calculate the shortest or fastest routes between locations based on factors like vehicle size and type, traffic conditions, and distance. Routing also supports multi-modal routing, which include multiple modes of transport in a single trip, including cycling, walking, and ferries. This functionality is used for location-based searches and route optimization in applications like fleet management, proximity marketing, and emergency services as well as logistics and delivery, urban planning, ride sharing apps, and outdoor activities. === Map visualization === The platform supports map visualizations that can be modified to reflect real-time data (including from IoT sensors) as well as historical data patterns. Visualizations include heat maps, street maps, satellite imagery and other custom data layers. Maps are rendered using raster or vector tiles which reduce the load of displaying large data sets or complex maps. This can be used in various applications in areas like transportation, smart cities, retail and marketing, public health, and environmental monitoring. For example, it can be used for tracking the spread of diseases or measuring the impact of changing climatic patterns. === Geofencing and spatial analytics === Azure Maps supports polygonal geofencing, which enables the definition of custom geographic boundaries. Geofenced areas can be monitored in real-time for events of interest. For example, an application could send an alert when equipment or persons enter or leave a defined area. Tools for analyzing historical geofencing data are also available via the APIs for optimization purposes. == Industry usage == Azure Maps' geofencing function has seen usage in the construction industry, designating hazardous areas for safety purposes and sending alerts if anyone enters the area. Private facility maps are used by construction companies for monitoring large construction sites to increase productivity and prevent accidents or damage. In emergency management, New Zealand based company Beca has used Azure Maps to provide analysis on the impact of earthquakes to users, including information on the severity and location of an earthquake and the impact on affected properties. Alaska's Department of Transportation uses Azure Maps as part of an information system providing weather-related warnings and analytics to road crews. Airmap, an airspace management platform for drones, uses Azure Maps. Azure Maps has also been used in conjunction with Azure Monitor for risk monitoring by an insurance company. Other companies that use or have used Azure Maps include BMW, Banco Santander, Jvion, MV Transportation, C.H. Robertson, Wise Skulls, Tata Consultancy Services, Providence Health and Services, Gas Brasiliano Distribuidora S.A., Shell plc, Persistent Systems, Phase 2 Dining and Entertainment, Symbio, HID, Globant, and Insight Enterprises. == Partnerships == Azure Maps and TomTom have been partners since 2016, and TomTom provides location data to Azure Maps and can process data from Azure Maps for mapping purposes. In 2021, Azure Maps partnered with AccuWeather to make climatic data available via its APIs, making weather data along all parts of calculated routes available for mobility and logistics purposes. Microsoft has partnered with Esri, the developer of ArcGIS, and there is cross-compatibility between Azure and ArcGIS so that data from Azure Maps can be integrated into ArcGIS and vice versa. Azure Maps partnered with Moovit in 2019, a startup providing software that interfaces with public transport data. Moovit's database on global public transit networks, including information on which stations and facilities are wheelchair accessible, was linked to Azure Maps. This service was noted for its use increasing accessibility to public transport for the visually impaired by means of voice activated route planning assistance. NORAD has used some Azure Maps functions for their NORAD Tracks Santa website during Christmas holidays. == Components == === REST APIs === Various APIs cover the major functionalities across Azure Maps: Data registry API Geolocation API Render API Route API Search API Spatial API Time zone API Traffic API Weather API === SDKs === Azure Maps SDKs uses MapLibre-style specifications and open source MapLibre GL-based libraries as a rendering engine. The Web SDK is used for developing web apps with maps and location-based data and functionality. It includes a map control module as well as modules with drawing tools. It also supports Azure Maps Creator and various spatial data formats. The platform also includes a set of REST SDKs for developers integrating Azure Maps REST APIs into Python, C#, Java or JavaScript applications. Azure Maps also includes Android and iOS SDKs used for developing applications for Android and Apple devices. === Azure Maps Creator === Azure Maps Creator is a tool for generating custom maps for locations like large office complexes, construction sites, or university campuses. These maps can then be integrated into applications and used with other Azure Maps functions for purposes such as wayfinding and maintenance and security in building automation contexts. === Azure Maps Visual for Power BI === Azure Maps is integrated with Microsoft Power BI, a graphical tool for producing data visualizations. Since July 2020, Power BI can be used in conjunction with Azure Maps for developing map-based data visualizations. This functionality entered general availability in May 2023.
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ASR-complete

ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term is typically used informally. Such problems are hypothesised to include: Spoken natural language understanding Understanding speech from far-field microphones, i.e. handling the reverbation and background noise These problems are easy for humans to do (in fact, they are described directly in terms of imitating humans). Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.
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Poop Map

Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.
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Biopython

Biopython is an open-source collection of non-commercial Python modules for computational biology and bioinformatics. It makes robust and well-tested code easily accessible to researchers. Python is an object-oriented programming language and is a suitable choice for automation of common tasks. The availability of reusable libraries saves development time and lets researchers focus on addressing scientific questions. Biopython is constantly updated and maintained by a large team of volunteers across the globe. Biopython contains parsers for diverse bioinformatic sequence, alignment, and structure formats. Sequence formats include FASTA, FASTQ, GenBank, and EMBL. Alignment formats include Clustal, BLAST, PHYLIP, and NEXUS. Structural formats include the PDB, which contains the 3D atomic coordinates of the macromolecules. It has provisions to access information from biological databases like NCBI, Expasy, PBD, and BioSQL. This can be used in scripts or incorporated into their software. Biopython contains a standard sequence class, sequence alignment, and motif analysis tools. It also has clustering algorithms, a module for structural biology, and a module for phylogenetics analysis. == History == The development of Biopython began in 1999, and it was first released in July 2000. First "semi-complete" and "semi-stable" release was done in March 2001 and December 2002 respectively. It was developed during a similar time frame and with analogous goals to other projects that added bioinformatics capabilities to their respective programming languages, including BioPerl, BioRuby and BioJava. Early developers on the project included Jeff Chang, Andrew Dalke and Brad Chapman, though over 100 people have made contributions to date. In 2007, a similar Python project, namely PyCogent, was established. The initial scope of Biopython involved accessing, indexing and processing biological sequence files. The retrieved data from common biological databases will then be parsed into a python data structure. While this is still a major focus, over the following years added modules have extended its functionality to cover additional areas of biology. The key challenge in the design of parsers for bioinformatics file formats is the frequency at which the data formats change. This is due to inadequate curation of the structure of the data, and changes in the database contents. This problem is overcome by the application of a standard event-oriented parser design (see Key features and examples). As of version 1.77, Biopython no longer supports Python 2. The current stable release of Biopython version 1.85 was released on 15 January 2025. It only supports Python 3 and the recent releases of Biopython require NumPy (and not Numeric). == Design == Wherever possible, Biopython follows the conventions used by the Python programming language to make it easier for users familiar with Python. For example, Seq and SeqRecord objects can be manipulated via slicing, in a manner similar to Python's strings and lists. It is also designed to be functionally similar to other Bio projects, such as BioPerl. It is organized into modular sub-packages, e.g., Bio.Seq, Bio.Align, Bio.PDB, Bio.Entrez each of them useful in a different bioinformatics domain. It used principles, like encapsulation and polymorphism, notably in classes Seq, SeqRecord, and Bio.PDB.Structure. It can also interoperate with other Python tools (Pandas, Matplotlib and SciPy). Biopython can read and write most common file formats for each of its functional areas, and its license is permissive and compatible with most other software licenses, which allows Biopython to be used in a variety of software projects. == Requirements == Biopython is currently supported and tested with the following Python implementations: Python 3 or PyPy3 NumPy == Key features and examples == === Input and output === Biopython can read and write to a number of common formats. When reading files, descriptive information in the file is used to populate the members of Biopython classes, such as SeqRecord. This allows records of one file format to be converted into others. Very large sequence files can exceed a computer's memory resources, so Biopython provides various options for accessing records in large files. They can be loaded entirely into memory in Python data structures, such as lists or dictionaries, providing fast access at the cost of memory usage. Alternatively, the files can be read from disk as needed, with slower performance but lower memory requirements. === Sequences === A core concept in Biopython is the biological sequence, and this is represented by the Seq class. A Biopython Seq object is similar to a Python string in many respects: it supports the Python slice notation, can be concatenated with other sequences and is immutable. This object includes both general string-like and biological sequence-specific methods. It is best to store information about the biological type (DNA, RNA, protein) separately from the sequence, rather than using an explicit alphabet argument. === Sequence annotation === The SeqRecord class describes sequences, along with information such as name, description and features in the form of SeqFeature objects. Each SeqFeature object specifies the type of the feature and its location. Feature types can be ‘gene’, ‘CDS’ (coding sequence), ‘repeat_region’, ‘mobile_element’ or others, and the position of features in the sequence can be exact or approximate. === Accessing online databases === Through the Bio.Entrez module, users of Biopython can download biological data from NCBI databases. Each of the functions provided by the Entrez search engine is available through functions in this module, including searching for and downloading records. === Phylogeny === The Bio.Phylo module provides tools for working with and visualising phylogenetic trees. A variety of file formats are supported for reading and writing, including Newick, NEXUS and phyloXML. Common tree manipulations and traversals are supported via the Tree and Clade objects. Examples include converting and collating tree files, extracting subsets from a tree, changing a tree's root, and analysing branch features such as length or score. Rooted trees can be drawn in ASCII or using matplotlib (see Figure 1), and the Graphviz library can be used to create unrooted layouts (see Figure 2). === Genome diagrams === The GenomeDiagram module provides methods of visualising sequences within Biopython. Sequences can be drawn in a linear or circular form (see Figure 3), and many output formats are supported, including PDF and PNG. Diagrams are created by making tracks and then adding sequence features to those tracks. By looping over a sequence's features and using their attributes to decide if and how they are added to the diagram's tracks, one can exercise much control over the appearance of the final diagram. Cross-links can be drawn between different tracks, allowing one to compare multiple sequences in a single diagram. === Macromolecular structure === The Bio.PDB module can load molecular structures from PDB and mmCIF files, and was added to Biopython in 2003. The Structure object is central to this module, and it organises macromolecular structure in a hierarchical fashion: Structure objects contain Model objects which contain Chain objects which contain Residue objects which contain Atom objects. Disordered residues and atoms get their own classes, DisorderedResidue and DisorderedAtom, that describe their uncertain positions. Using Bio.PDB, one can navigate through individual components of a macromolecular structure file, such as examining each atom in a protein. Common analyses can be carried out, such as measuring distances or angles, comparing residues and calculating residue depth. === Population genetics === The Bio.PopGen module adds support to Biopython for Genepop, a software package for statistical analysis of population genetics. This allows for analyses of Hardy–Weinberg equilibrium, linkage disequilibrium and other features of a population's allele frequencies. This module can also carry out population genetic simulations using coalescent theory with the fastsimcoal2 program. === Wrappers for command line tools === Biopython previously included command-line wrappers for tools such as BLAST, Clustal, EMBOSS, and SAMtools. This option allowed users to run external tool commands from within the code using specialized Biopython classes. However, Bio.Application modules and their wrappers have deprecated and will be removed in future Biopython releases. The main reason for this is the high maintenance burden of updating them with the evolving external tools. The recommended approach is to directly construct and execute command-line tool commands using Python’s built-in subprocess module. This method provides flexibility and removes the dependency on the Biopython wrappers. subprocess is a native Python module useful for running ext
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Software durability

In software engineering, software durability means the solution ability of serviceability of software and to meet user's needs for a relatively long time. Software durability is important for user's satisfaction. For a software security to be durable, it must allow an organization to adjust the software to business needs that are constantly evolving, often in impulsive ways. Durability of software depends on four characteristics mainly; i.e. software trustworthiness, Human Trust for Serviceability, software dependability and software usability.
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Digital on-screen graphics by country

Digital on-screen graphics by country are the varying logos and differences of digital on-screen graphics in different countries and regions. == Overview == Digital on-screen graphics (DOGs; also called a digitally originated graphic, bug, network bug, on-screen bug, or screenbug) are almost always placed in one of four corners: the top left, the top right, the bottom left, or the bottom right. There are few exceptions to this rule: most notably, Saturday! in Russia, which places their DOG in the top center. Many news broadcasters, as well as a few television networks, also place a clock alongside their bug. In the United States, Canada, Australia, and New Zealand, DOGs may also include the show's parental guideline rating. In Australia, this is known as a Program Return Graphic (PRG). It has become common to place text above the station's logo advertising other programs on the network. In many countries, some TV networks insert the word "live" near the DOG to advise viewers that the program is live, rather than pre-recorded. During televised sports events, a DOG may also display game-related statistics such as the current score. This has led people in Canada and the United States to refer to such a DOG as a score bug. In many countries, DOGs are removed in non-program sections such as commercials and program trailers, but TV channels in some other countries have retained in full color or instead replaced them in either of these sections or in both sections (like Turkey, Indonesia, Italy, the entirety of South Asia, Vietnam, Taiwan, and Russia). == MENA == === Arab world === Arabic TV logos are placed in the top-right and top-left except for Al-Jazeera, whose logo appears on the bottom-right of the screen. Some Arabian TV stations hide their logos during commercial breaks and promos/trailers, such as Dubai TV, Dubai One, Funoon, the Egyptian CBC and Nile TV networks, ART Hekayat, ART Hekayat 2, Iqraa, and Al-Jazeera. Abu Dhabi TV and MBC1 initially had their logos at the bottom-right corner from their launch until the mid-2000s, when they were moved to the top-right corner. === Iran === Iranian broadcaster IRIB introduced DOGs in early 2000s. Unlike other Middle Eastern nations that introduced DOGs on their TV networks in 1990s, Iran was very late in this practice. Almost all Iranian TV channels display DOGs at top-left corner of the screen. The few exception is IRIB-owned channels remove DOGs during news broadcasts. === Israel === In Israel, Television DOGs were first introduced in 1991. Israeli channel watermarks most often appear on the top left or the top right corner since Israeli cable and satellite-based services often have the channel description and programming (OSD) on the bottom of the screen. Most channels have an opaque, full-color watermark, though exceptions exist, for example Channel 9, which displays a blue-tinted semi-transparent logo. In ad breaks, it is required to replace the channel watermark with another symbol – sometimes on the other edge of the screen – indicating there are ads at the moment. The Israel Broadcasting Authority, whose channels placed their logos in the top left corner, ceased broadcasting in May 2017. The new public broadcaster, the Israeli Public Broadcasting Corporation, displays its logos at the top right instead. The erstwhile Channel 2 as well as its successors, Keshet 12 and Reshet 13, also use the top right corner. However, Channel 10 used the top left corner before rebranding to Eser (Literally "Ten") in 2017 and simultaneously moving its logo to the top right (Not long after, in January 2019, it ceased broadcasting as it merged with Reshet 13). Channel 14 as well as its predecessor Channel 20 use the top right corner as well. The Knesset Channel, however, uses the top left corner. === Morocco === The SNRT and 2M And Al-Aoula Uses permanent on-screen DOGs for their TV channels. In contrast, other channels such as Medi 1 TV hide their DOGs during commercial breaks. == Asia == === Brunei === Radio Television Brunei introduced DOGs in 1994. Like TV channels from neighbouring Malaysia, all DOGs are removed during advertisement breaks. === Cambodia === Cambodian TV channels introduced DOGs in 1995. Like Thailand, all logos are full-color and displayed on the top-right corner of the screen. Some channels such as TV5 hide their logos during commercial breaks. Hang Meas HDTV Logo on the top-left corner of the screen, CTN (Cambodian Television Network), MyTV, Bayon TV, PNN, Logo on the top-right corner of the screen. === China === TV stations in mainland China always place their logo (usually semi-transparent and sometimes animated) in the top-left corner of the screen in full-color or grey-scale. Regardless of the content being broadcast (program or advertisements), some channels like Phoenix Television hide their logos during commercial breaks; although in some rare cases, the DOG may be placed elsewhere to avoid covering the score bug during the broadcast of a sporting event. China introduced logos in 1983 on the bottom-left corner of the screen, but they were used only during commercial breaks and clock idents. Later China Central Television (CCTV) introduced permanent DOGs for all programs in 1992, on the top-left corner of the screen. China also displays a clock on top-right corner of the screen for 1 minute between 59:30–00:30 & 29:30–30:30 time in transition between programs. === Hong Kong === Hong Kong TV introduced DOGs in 1994. Hong Kong DOGs can be either of full color or semi-transparent and (except for RTHK 31) always be hidden during commercial breaks. Television Broadcasts Limited (TVB) placed their logos at the top-right corner of the screen while now-defunct Asia Television and other channels placed their logos at the top-left corner of the screen. Sometimes, weather information, date, and time clocks had been used alongside DOGs in news programs, continuity & live broadcasts. === India === The first on-screen logo in India was introduced in 1984 by DD2 Metro (now DD News). It was white and slightly transparent. All Indian TV channels have on-screen logos. They are always full-colors, never transparent, and they are almost never removed during commercial breaks (though the channels of the South Indian Sun TV Network did so until 2015). The great majority of Indian TV channels place their logos in the top right corner of the screen, though there are exceptions. The corner used may be broadcaster-dependent. Among the big national broadcasters: Channels from the Sony network always use the top right corner, without exception. Star channels also use the top right, with the exception of National Geographic and Nat Geo Wild, which use the top left corner in line with their international counterparts. Past exceptions include The History Channel, whose logo was placed in the top left until it rebranded to Fox History & Entertainment in 2008; the now-defunct Channel V, which used the top left between 2013 and 2016; and Nat Geo People, Nat Geo Music and BabyTV, were withdrawn from India in June 2019. TV18 and Viacom18 channels use the top right corner as well, with the exceptions of regional-language movie channels (e.g., Colors Kannada Cinema and Colors Gujarati Cinema) as well as Colors Super, which have shown their logos at the top left corner since 2018; and VH1, which has always used the bottom right corner. Also, CNBC-TV18, CNBC Awaaz and CNBC Bajar use the bottom right. Moreover, MTV showed its logo in the top left corner until 23 April 2018, when it was moved to the top right (its HD version, launched in 2017, has always used the top right). Unlike most other major networks, the Zee Network's non-news channels containing 'Zee' in their name display their logos at the top left corner and not the top right. This has been the case since 15 October 2017, when almost all the Zee-branded TV channels of the Zee network rebranded with a new logo and, in many cases, a new graphics package and look. Before then, the logos were shown at the top right, as with other broadcasters. (News channels' logos—i.e., logos of channels owned by Zee Media Corporation—stayed put at the top right corner, with the exception of WION, which uses the bottom left.) All the major Zee-branded channels—such as Zee TV, Zee Cinema, Zee Café and the regional-language channels like Zee Tamil, Zee Telugu, Zee Marathi and Zee Bangla—show their logos at the top left; moreover, the Odia-language channel Sarthak TV rebranded to Zee Sarthak and moved its logo to the top left. Among the Zee channels not containing the word 'Zee' that moved their logos to the top left during the big rebrand in 2017 was English movie channel Zee Studio; when it was renamed to &flix on 3 June 2018, the logo remained at the top left. Moreover, Hindi movie channel &pictures has always shown its logo at the top left since its launch in 2013. However, &privé HD, Zee's other English movie channel, and Hindi entertainment channel &TV place the
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FreshBooks

FreshBooks is accounting software operated by 2ndSite Inc. primarily for small and medium-sized businesses. It is a web-based software as a service (SaaS) model, that can be accessed through a desktop or mobile device. The company was founded in 2003 and is based in Toronto, Canada. == History == FreshBooks was founded in 2004 by Mike McDerment, Levi Cooperman, and Joe Sawada in Toronto, Ontario. McDerment incorporated a second company, BillSpring in January 2015 to work on new product development. It was rolled back into FreshBooks as an updated interface in 2016. Initially FreshBooks functioned like an electronic invoicing program targeting IT professionals. After the release of the new interface, the initial release of FreshBooks was referred to as "FreshBooks Classic." FreshBooks Classic was discontinued in 2022 after migrating users to the new platform. FreshBooks Classic's front-end application was built in PHP, and the backend services were built in Python while the new FreshBooks uses the same backend services with a JavaScript single-page application. == Product == FreshBooks is a subscription-based accounting software platform that provides features such as invoicing, accounts payable, expense and time tracking, retainers, fixed asset depreciation, purchase orders, payroll integrations, mileage tracking, double-entry accounting, and standard business reporting. Financial data is stored in the cloud on a unified ledger, enabling access from desktop and mobile devices. The platform includes a free API for integration with external applications and supports multiple tax rates and currencies. It also offers project management and payroll functionalities. Pricing is based on a recurring monthly fee. FreshBooks supports country-specific tax calculations, including GST and HST in Canada, sales taxes in the United States, and MTD compliance in the UK. == Operations == FreshBooks has its headquarters in Toronto, Canada with operations in North America, Europe and Australia. Founder Mike McDerment was the chief executive officer of the company from 2003 until 2021, when he stepped down and was replaced by Don Epperson, but stayed as the executive chair. Don Epperson had previously joined FreshBooks as executive director in 2019. == Funding == FreshBooks was initially self-funded. In 2014, the company raised a Series A venture investment of $30 million led by the venture capital firm Oak Investment Partners, with participation by Georgian Partners and Atlas Venture. In 2017, FreshBooks announced that it raised another $43 million in funding from Accomplice, Georgian Partners and Oak Investment Partners. On August 10, 2021, FreshBooks announced that it had secured $80.75 million in Series E funding and $50 million in debt financing. FreshBooks also reached a valuation of more than $1 billion.
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Color balance

In photography and image processing, color balance is the global adjustment of the intensities of the colors (typically red, green, and blue primary colors). An important goal of this adjustment is to render specific colors – particularly neutral colors like white or grey – correctly. Hence, the general method is sometimes called gray balance, neutral balance, or white balance. Color balance changes the overall mixture of colors in an image and is used for color correction. Generalized versions of color balance are used to correct colors other than neutrals or to deliberately change them for effect. White balance is one of the most common kinds of balancing, and is when colors are adjusted to make a white object (such as a piece of paper or a wall) appear white and not a shade of any other colour. Image data acquired by sensors – either film or electronic image sensors – must be transformed from the acquired values to new values that are appropriate for color reproduction or display. Several aspects of the acquisition and display process make such color correction essential – including that the acquisition sensors do not match the sensors in the human eye, that the properties of the display medium must be accounted for, and that the ambient viewing conditions of the acquisition differ from the display viewing conditions. The color balance operations in popular image editing applications usually operate directly on the red, green, and blue channel pixel values, without respect to any color sensing or reproduction model. In film photography, color balance is typically achieved by using color correction filters over the lights or on the camera lens. == Generalized color balance == Sometimes the adjustment to keep neutrals neutral is called white balance, and the phrase color balance refers to the adjustment that in addition makes other colors in a displayed image appear to have the same general appearance as the colors in an original scene. It is particularly important that neutral (gray, neutral, white) colors in a scene appear neutral in the reproduction. === Psychological color balance === Humans relate to flesh tones more critically than other colors. Trees, grass and sky can all be off without concern, but if human flesh tones are 'off' then the human subject can look sick or dead. To address this critical color balance issue, the tri-color primaries themselves are formulated to not balance as a true neutral color. The purpose of this color primary imbalance is to more faithfully reproduce the flesh tones through the entire brightness range. == Illuminant estimation and adaptation == Most digital cameras have means to select color correction based on the type of scene lighting, using either manual lighting selection, automatic white balance, or custom white balance. The algorithms for these processes perform generalized chromatic adaptation. Many methods exist for color balancing. Setting a button on a camera is a way for the user to indicate to the processor the nature of the scene lighting. Another option on some cameras is a button which one may press when the camera is pointed at a gray card or other neutral colored object. This captures an image of the ambient light, which enables a digital camera to set the correct color balance for that light. There is a large literature on how one might estimate the ambient lighting from the camera data and then use this information to transform the image data. A variety of algorithms have been proposed, and the quality of these has been debated. A few examples and examination of the references therein will lead the reader to many others. Examples are Retinex, an artificial neural network or a Bayesian method. == Chromatic colors == Color balancing an image affects not only the neutrals, but other colors as well. An image that is not color balanced is said to have a color cast, as everything in the image appears to have been shifted towards one color. Color balancing may be thought in terms of removing this color cast. Color balance is also related to color constancy. Algorithms and techniques used to attain color constancy are frequently used for color balancing, as well. Color constancy is, in turn, related to chromatic adaptation. Conceptually, color balancing consists of two steps: first, determining the illuminant under which an image was captured; and second, scaling the components (e.g., R, G, and B) of the image or otherwise transforming the components so they conform to the viewing illuminant. Viggiano found that white balancing in the camera's native RGB color model tended to produce less color inconstancy (i.e., less distortion of the colors) than in monitor RGB for over 4000 hypothetical sets of camera sensitivities. This difference typically amounted to a factor of more than two in favor of camera RGB. This means that it is advantageous to get color balance right at the time an image is captured, rather than edit later on a monitor. If one must color balance later, balancing the raw image data will tend to produce less distortion of chromatic colors than balancing in monitor RGB. == Mathematics of color balance == Color balancing is sometimes performed on a three-component image (e.g., RGB) using a 3x3 matrix. This type of transformation is appropriate if the image was captured using the wrong white balance setting on a digital camera, or through a color filter. Changing the color balance of an image can improve classifier results on a trained ML model. === Scaling monitor R, G, and B === In principle, one wants to scale all relative luminances in an image so that objects which are believed to be neutral appear so. If, say, a surface with R = 240 {\displaystyle R=240} was believed to be a white object, and if 255 is the count which corresponds to white, one could multiply all red values by 255/240. Doing analogously for green and blue would result, at least in theory, in a color balanced image. In this type of transformation the 3x3 matrix is a diagonal matrix. [ R G B ] = [ 255 / R w ′ 0 0 0 255 / G w ′ 0 0 0 255 / B w ′ ] [ R ′ G ′ B ′ ] {\displaystyle \left[{\begin{array}{c}R\\G\\B\end{array}}\right]=\left[{\begin{array}{ccc}255/R'_{w}&0&0\\0&255/G'_{w}&0\\0&0&255/B'_{w}\end{array}}\right]\left[{\begin{array}{c}R'\\G'\\B'\end{array}}\right]} where R {\displaystyle R} , G {\displaystyle G} , and B {\displaystyle B} are the color balanced red, green, and blue components of a pixel in the image; R ′ {\displaystyle R'} , G ′ {\displaystyle G'} , and B ′ {\displaystyle B'} are the red, green, and blue components of the image before color balancing, and R w ′ {\displaystyle R'_{w}} , G w ′ {\displaystyle G'_{w}} , and B w ′ {\displaystyle B'_{w}} are the red, green, and blue components of a pixel which is believed to be a white surface in the image before color balancing. This is a simple scaling of the red, green, and blue channels, and is why color balance tools in Photoshop have a white eyedropper tool. It has been demonstrated that performing the white balancing in the phosphor set assumed by sRGB tends to produce large errors in chromatic colors, even though it can render the neutral surfaces perfectly neutral. === Scaling X, Y, Z === If the image may be transformed into CIE XYZ tristimulus values, the color balancing may be performed there. This has been termed a "wrong von Kries" transformation. Although it has been demonstrated to offer usually poorer results than balancing in monitor RGB, it is mentioned here as a bridge to other things. Mathematically, one computes: [ X Y Z ] = [ X w / X w ′ 0 0 0 Y w / Y w ′ 0 0 0 Z w / Z w ′ ] [ X ′ Y ′ Z ′ ] {\displaystyle \left[{\begin{array}{c}X\\Y\\Z\end{array}}\right]=\left[{\begin{array}{ccc}X_{w}/X'_{w}&0&0\\0&Y_{w}/Y'_{w}&0\\0&0&Z_{w}/Z'_{w}\end{array}}\right]\left[{\begin{array}{c}X'\\Y'\\Z'\end{array}}\right]} where X {\displaystyle X} , Y {\displaystyle Y} , and Z {\displaystyle Z} are the color-balanced tristimulus values; X w {\displaystyle X_{w}} , Y w {\displaystyle Y_{w}} , and Z w {\displaystyle Z_{w}} are the tristimulus values of the viewing illuminant (the white point to which the image is being transformed to conform to); X w ′ {\displaystyle X'_{w}} , Y w ′ {\displaystyle Y'_{w}} , and Z w ′ {\displaystyle Z'_{w}} are the tristimulus values of an object believed to be white in the un-color-balanced image, and X ′ {\displaystyle X'} , Y ′ {\displaystyle Y'} , and Z ′ {\displaystyle Z'} are the tristimulus values of a pixel in the un-color-balanced image. If the tristimulus values of the monitor primaries are in a matrix P {\displaystyle \mathbf {P} } so that: [ X Y Z ] = P [ L R L G L B ] {\displaystyle \left[{\begin{array}{c}X\\Y\\Z\end{array}}\right]=\mathbf {P} \left[{\begin{array}{c}L_{R}\\L_{G}\\L_{B}\end{array}}\right]} where L R {\displaystyle L_{R}} , L G {\displaystyle L_{G}} , and L B {\displaystyle L_{B}} are the un-gamma corrected monitor RGB, one may use: [ L R L G L B ] = P − 1 [ X w / X w ′ 0 0
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