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  Paint.NET

  Paint.NET (sometimes stylized as paint.net) is a freeware general-purpose raster graphics editor program for Microsoft Windows, developed with the .NET platform. Paint.NET was originally created by Rick Brewster as a Washington State University student project, and has evolved from a simple replacement for the Microsoft Paint program into a program for editing mainly graphics, with support for plugins. == History == Paint.NET originated as a computer science senior design project by Rick Brewster during spring 2004 at Washington State University. Version 1.0 consisted of 36,000 lines of code and was written in four months. In contrast, version 3.35 has approximately 162,000 lines of code. The Paint.NET project continued over the summer and into the autumn 2004 semester for both the version 1.1 and 2.0 releases. Development continued with one programmer who worked on previous versions of Paint.NET while he was a student at WSU. As of May 2006 the program had been downloaded at least 2 million times, at a rate of about 180,000 per month. Initially, Paint.NET was released under a modified version of the MIT License, with the exclusion of the installer, text, and graphics. However, citing issues with the open source code being plagiarized by others that had rebranded the software as their own and bundled user content without their permission, the availability of the source code was restricted, in December 2007 Brewster announced his intent to restrict access to components of the program (including its installer, resources, and user interface). In November 2009, the software was made proprietary, restricting the sale or creation of derivative works of the software. Starting with version 4.0.18, Paint.NET is published in two editions: A classic edition remains freeware, similar to all other versions since 3.5. Another edition, however, is published to Microsoft Store under a trialware license and is available to purchase for US$14.99. According to the developer, this was done to enable the users to contribute to the development with more convenience, even though the old avenue of donation was not closed. In May 2026, Brewster revealed that he obtained the paint.net domain after attempting to do so for 22 years. Historically, the editor was hosted on getpaint.net, and according to Brewster, the previous owners of paint.net would not sell the domain and asked for "lots and lots of money". In December of the previous year, paint.net began hosting content that impersonated Paint.NET, therefore becoming a clear case of trademark infringement and domain squatting. Brewster stated that he was able to obtain the domain afterwards with the help of a lawyer. == Overview == Paint.NET is primarily programmed in the C# programming language. Its native image format, .PDN, is a compressed representation of the application's internal object format, which preserves layering and other information. == Plugins == Paint.NET supports plugins, which add image adjustments, effects, and support for additional file types. They can be programmed using any .NET Framework programming language, though they are most commonly written in C#. These are created by volunteer coders on the program's discussion board, the Paint.NET Forum. Though most are simply published via the discussion board, some have been included with a later release of the program. For instance, a DirectDraw Surface file type plugin, (originally by Dean Ashton) and an Ink Sketch and Soften Portrait effect (originally by David Issel) were added to Paint.NET in version 3.10. Hundreds of plugins have been produced; such as Shape3D, which renders a 2D drawing into a 3D shape. Some plugins expand on the functionality that comes with Paint.NET, such as Curves+ and Sharpen+, which extend the included tools Curves and Sharpen, respectively. Examples of file type plugins include an Animated Cursor and Icon plugin and an Adobe Photoshop file format plugin. Several of these plugins are based on existing open source software, such as a raw image format plugin that uses dcraw and a PNG optimization plugin that uses OptiPNG. == Forks == === paint-mono === Paint.NET was created exclusively for Windows and has no native support for other operating systems. Due to its former open-source licensing, the development of alternative versions was possible. In May 2007, Miguel de Icaza officially started a porting project called paint-mono. This project had partially ported Paint.NET 3.0 to Mono, an open-source implementation of the Common Language Infrastructure on which the .NET Framework is based. This allowed Paint.NET to be run on Mono-supported platforms, such as Linux. This port is no longer maintained and has not been updated since March 2009. Newer Mono runtime 6 versions are able to run original Paint.NET releases up to 3.5.11 with only minor issues. === Pinta === In 2010, developer Jonathan Pobst started a project called Pinta, describing it as a clone of Paint.NET for Mono and Gtk#. Pinta reused the adjustments and effects code from Paint.NET but otherwise is original code.

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  SAP BTP

  SAP Business Technology Platform (SAP BTP) is a platform as a service developed by SAP SE that offers a suite of services including database and data management, AI, analytics, application development, automation and integration all running on one unified platform. == Overview == SAP BTP is made up of four components: Application development and automation: to create applications or extend existing applications. Data and analytics: to access and analyze data across SAP and third-party systems using multi-cloud architecture. Integration: to integrate and connect applications and data. Artificial Intelligence (AI): to access large language models (LLMs) to develop AI. == History == SAP BTP was introduced as part of the SAP strategy to unify its portfolio and cloud offerings under a single platform. The platform was evolved from earlier initiatives such as SAP Cloud Platform and now serves as the central hub for cloud, data, analytics, integration and AI technologies. Initially unveiled as "SAP NetWeaver Cloud" belonging to the SAP HANA Cloud portfolio on October 16, 2012 the cloud platform was reintroduced with the new name "SAP HANA Cloud Platform" on May 13, 2013 as the foundation for SAP cloud products, including the SAP BusinessObjects Cloud. Adoption of the SAP HANA Cloud Platform in 2015 stood at over 4000 customers and 500 partners. In 2016, SAP and Apple Inc. partnered to develop mobile applications on iOS using cloud-based software development kits (SDKs) for the SAP Cloud Platform. On February 27, 2017, SAP HANA Cloud Platform was renamed "SAP Cloud Platform" at the Mobile World Congress. On January 18, 2021, the name "SAP Cloud Platform" was retired from the SAP product portfolio to support SAP BTP. As of October 2024, SAP states that SAP BTP is used by more than 27,000 customers and more than 2,800 partners. Recently, SAP Business One has worked on improving the functionalities of BTP to cater for the demands of digital transformation. The platform offers comprehensive services in AI, application development, automation, integration, data management, and analytics.

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  Pandas (software)

  Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals, as well as a play on the phrase "Python data analysis". Wes McKinney started building what would become Pandas at AQR Capital while he was a researcher there from 2007 to 2010. The development of Pandas introduced into Python many comparable features of working with DataFrames that were established in the R programming language. The library is built upon another library, NumPy. == History == Developer Wes McKinney started working on Pandas in 2008 while at AQR Capital Management out of the need for a high performance, flexible tool to perform quantitative analysis on financial data. Before leaving AQR, he was able to convince management to allow him to open source the library in 2009. Another AQR employee, Chang She, joined the effort in 2012 as the second major contributor to the library. In 2015, Pandas signed on as a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. == Data model == Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. === Series === A Series is a one-dimensional array-like object that stores a sequence of values together with an associated set of labels, called an index. It is built on top of NumPy's array and affords many similar functionalities, but instead of using implicit integer positions, a Series allows explicit index labels of many data types. A Series can be created from Python lists, dictionaries, or NumPy arrays. If no index is provided, pandas automatically assigns a default integer index ranging from 0 to n-1, where n is the number of items in the Series. A simple example with customized labels is: To access a value or list of values from a Series, use its index or list of indices: Series can be used arithmetically, as in the statement series_3 = series_1 + series_2. This will align data points with corresponding index values in series_1 and series_2 (similar to a join in relational algebra), then add them together to produce new values in series_3. A Series has various attributes, such as name (Series name), dtype (data type of values), shape (number of rows), values, and index. They can be used in many of the same operations as NumPy arrays, with additional methods for reindexing, label-based selection, and handling missing data. === DataFrame === A DataFrame is a two-dimensional, tabular data structure with labeled rows and columns. Each column is stored internally as a Series and may hold a different data type (numeric, string, boolean, etc.). DataFrames can be created by a variety of means, including dictionaries of lists, NumPy arrays, and external files such as CSV or Excel spreadsheets: To retrieve a DataFrame column as a Series, use either 1) the index (dict-like notation) or 2) the name of column if the name is a valid Python identifier (attribute-like access). DataFrames support operations such as column assignment, row and column deletion, label-based indexing with loc, position-based indexing with iloc, reshaping, grouping, and joining. Merge operations implement a subset of relational algebra and allow one-to-one, many-to-one, and many-to-many joins. Some common attributes of a DataFrame include dtypes (data type of each column), shape (dimensions of the DataFrame returned as a tuple with form (number of rows, number of columns)), index/columns (labels of the DataFrame's rows/columns, respectively, returned as an Index object), values (data in the DataFrame returned as a 2D array), and empty (returns True if the DataFrame is empty). === Index === Index objects hold metadata for Series and Dataframe objects, such as axis labels and names, and are automatically created from input data. By default, a pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can also use any NumPy data type, including floating point, timestamps, or strings. Indices are also immutable, which allows them to be safely shared across multiple objects. pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values. For example, if s is a Series, s['a'] will return the data point at index a. Unlike dictionary keys, index values are not guaranteed to be unique. If a Series uses the index value a for multiple data points, then s['a'] will instead return a new Series containing all matching values. A DataFrame's column names are stored and implemented identically to an index. As such, a DataFrame can be thought of as having two indices: one column-based and one row-based. Because column names are stored as an index, these are not required to be unique. If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it is actually defined. pandas also supports hierarchical indices with multiple values per data point through the "MultiIndex" class. MultiIndex objects allow a single DataFrame to represent multiple dimensions, similar to a pivot table in Microsoft Excel, where each level can optionally carry its own unique name. In practice, data with more than 2 dimensions is often represented using DataFrames with hierarchical indices, instead of the higher-dimension Panel and Panel4D data structures. == Functionality == pandas supports a variety of indexing and subsetting techniques, allowing data to be selected by label, index, or Boolean conditions. For example, df[df['col1'] > 5] will return all rows in the DataFrame df for which the value of the column col1 exceeds 5. The library also implements grouping operations based on the split-apply-combine approach, enabling users to aggregate, transform, or restructure data according to column values or functions applied to index labels. For example, df['col1'].groupby(df['col2']) groups the data in 'col1' by their values in 'col2', while df.groupby(lambda i: i % 2) groups all data in the whole DataFrame by whether their index is even. The library also provides extensive tools for transforming, filtering and summarizing data. Users may apply arbitrary functions to Series and DataFrames, and because the library is built on top of Numpy, most NumPy functions can be applied directly to pandas objects as well. The library also includes built-in operations for arithmetic operations, string processing, and descriptive statistics such as mean, median, and standard deviation. These built-in functions are designed to handle missing data, usually represented by the floating-point value NaN. In addition, pandas includes tools for reorganizing data into different structural formats, with methods that can reshape tabular data between "wide" and "long" formats and pivot values based on column labels. pandas also implements a flexible set of relational operations for combining datasets. For instance, merge() links row in DataFrames based on one or more shared keys or indices, supporting one-to-one, one-to-many, and many-to-many relationships in a manner analogous to join operations in relational databases like SQL. DataFrames can also be concatenated or stacked together along an axis through the concat() method, and overlapping data can be further spliced together using combine_first() to fill in missing values. Furthermore, the library includes specialized support for working with time-series data. Features include the ability to interpolate values and filter using a range of timestamps, such as data['1/1/2023':'2/2/2023'] , which will return all dates between January 1 and February 2. Missing values in time-series data are represented by a dedicated NaT (Not a Timestamp) object, instead of the NaN value it uses elsewhere. == Criticisms == Pandas has been criticized for its inefficiency. The entire dataset must be loaded in RAM, and the library does not optimize query plans or support parallel computing across multiple cores. Wes McKinney, the creator of Pandas, has recommended Apache Arrow as an alternative to address these performance concerns and ot

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  D4Science

  D4Science is a Data Infrastructure offering services by community-driven virtual research environments. In particular, it supports communities of practice willing to implement open science practices, thus it is an Open Science Infrastructure. The infrastructure follows the system of systems approach, where the constituent systems (Service providers) offer "resources" (namely services and by them data, computing, storage) assembled together to implement the overall set of D4Science services. In particular, D4Science aggregates "domain agnostic" service providers as well as community-specific ones to build a unifying space where the aggregated resources can be exploited via Virtual research Environments and their services. It is spread across several sites, the primary one is hosted by the Istituto di Scienza e Tecnologie dell'Informazione of National Research Council (Italy). At the earth of this infrastructure there is an Open Source Software named gCube system. == Services == D4Science offers: Virtual Research Environment as a Service providing any community of practice with a dedicated working environment supporting any knowledge production process in a collaborative way, in fact every VRE enables computer-supported cooperative work by design. D4Science-based VREs are web-based, community-oriented, collaborative, user-friendly, open-science-enabler working environments for scientists and practitioners willing to work together to perform a set of (research) task. From the end-user perspective, each VRE manifests in a unifying web application (and a set of application programming interfaces (APIs)): (a) comprising several applications organised in specific menu items and (b) running in a plain web browser. Every application is providing VRE users with facilities implemented by relying on one or more services provisioned by diverse providers. Among the basic services every VRE is equipped with there are a Social Networking area enabling collaborative and open discussions on any topic and disseminating information of interest for the community, for example, the availability of a research outcome; a Workspace for storing, organizing and sharing any version of a research artifact, including dataset and model implementation; a User Management dashboard for managing membership and roles; a Catalogue Service recording the assets worth being published thus to make it possible for others to be informed and make use of these assets. Science Gateway as a Service providing a community of practice with a dedicated science gateway hosting a selected set of virtual research environments. Data Analytics at scale for data analytics including: a proprietary data analytics platform (DataMiner) to execute analytics tasks either by relying on methods provided by the user or by others. It is endowed with importing and sharing facilities for analytics methods implemented in heterogeneous forms including R, Java, Python, and KNIME. The platform enacts tasks execution by a distributed and hybrid computing infrastructure. Moreover, one of the worth highlighting feature of this platform is its open science-friendliness. All the analytics methods integrated in it are exposed by a standard protocol (the OGC WPS protocol) clients can use to get informed on available methods as well as to start processes, monitor their execution and access results. Every analytics task performed by the platform automatically produces a provenance record catering for the reproducibility of the task; an RStudio-based development environment for R enabling to perform statistical computing tasks in the cloud. This RStudio environment is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides seamless access to the VRE Workspace enabling sharing of resources with other members of the same working environment. a Jupyter-based notebook environment for developing and executing interactive computing by JupyterLab instances. Each JupyterLab is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides access to the VRE Workspace enabling sharing of resources with other members of the same working environment. == Community == The D4Science Infrastructure serves more than 24,000 registered users (August 2024) through 177 active VREs offered via 20 Science gateways. This extensive infrastructure not only supports a diverse range of scientific communities but also fosters significant engagement and collaboration among researchers worldwide. Engagement within the D4Science community is robust, with users benefiting from user-friendly application environments tailored to their specific needs. The platform allows users to securely preserve, access, and share their data from anywhere, fostering a collaborative and inclusive research environment. Additionally, groups of users can create their own virtual environments and customise them with the applications they need, further enhancing the platform's flexibility and usability. Supported communities and cases range from Agri-food to Social Data Science, Earth Science and Marine Science. These diverse applications demonstrate the versatility and broad applicability of the D4Science Infrastructure, making it an invaluable resource for researchers across various scientific domains. == History == The D4Science development has been supported by several European-funded projects. DILIGENT (2004-2007) in the Sixth Framework Programme for Research and Technological Development was the forerunner where a testbed infrastructure built by integrating digital library and grid computing technologies and resources was conceived and developed to serve the needs of communities of practice involved in knowledge development. In the context of the Seventh Framework Programme for research, technological development and demonstration the development of the D4Science initiative. In this period the infrastructure was established and developed to serve communities of practices from domains ranging from Earth Science to Marine Science with worldwide scope In the context of the H2020 research and innovation programme the maturity level of the D4Science infrastructure was high enough to allow a large and very diverse set of communities of practice to benefit from it and its services and further contribute to its development. Moreover, the services offered by the infrastructure have been developed to support open science practices. The operation and improvement of the D4Science infrastructure facilities are still ongoing while its exploitation is progressively growing.

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  Text normalization

  Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text is to be normalized and how it is to be processed afterwards; there is no all-purpose normalization procedure. == Applications == Text normalization is frequently used when converting text to speech. Numbers, dates, acronyms, and abbreviations are non-standard "words" that need to be pronounced differently depending on context. For example: "$200" would be pronounced as "two hundred dollars" in English, but as "lua selau tālā" in Samoan. "vi" could be pronounced as "vie," "vee," or "the sixth" depending on the surrounding words. Text can also be normalized for storing and searching in a database. For instance, if a search for "resume" is to match the word "résumé," then the text would be normalized by removing diacritical marks; and if "john" is to match "John", the text would be converted to a single case. To prepare text for searching, it might also be stemmed (e.g. converting "flew" and "flying" both into "fly"), canonicalized (e.g. consistently using American or British English spelling), or have stop words removed. == Techniques == For simple, context-independent normalization, such as removing non-alphanumeric characters or diacritical marks, regular expressions would suffice. For example, the sed script sed ‑e "s/\s+/ /g" inputfile would normalize runs of whitespace characters into a single space. More complex normalization requires correspondingly complicated algorithms, including domain knowledge of the language and vocabulary being normalized. Among other approaches, text normalization has been modeled as a problem of tokenizing and tagging streams of text and as a special case of machine translation. == Textual scholarship == In the field of textual scholarship and the editing of historic texts, the term "normalization" implies a degree of modernization and standardization – for example in the extension of scribal abbreviations and the transliteration of the archaic glyphs typically found in manuscript and early printed sources. A normalized edition is therefore distinguished from a diplomatic edition (or semi-diplomatic edition), in which some attempt is made to preserve these features. The aim is to strike an appropriate balance between, on the one hand, rigorous fidelity to the source text (including, for example, the preservation of enigmatic and ambiguous elements); and, on the other, producing a new text that will be comprehensible and accessible to the modern reader. The extent of normalization is therefore at the discretion of the editor, and will vary. Some editors, for example, choose to modernize archaic spellings and punctuation, but others do not. An edition of a text might be normalized based on internal criteria, where orthography is standardized according to the language of the original, or external criteria, where the norms of a different time period are applied. For an example of the latter, a published edition of a medieval Icelandic manuscript might be normalized to the conventions of modern Icelandic, or it might be normalized to Classical Old Icelandic. Standards of normalization vary based on language of the edition as well as the specific conventions of the publisher.

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  Dave's Redistricting

  Dave's Redistricting App (DRA) is an online web app originally created by Dave Bradlee that allows anyone to simulate redistricting a U.S. state's congressional and legislative districts. == Purpose == According to Bradlee, the software was designed to "put power in people's hands," and so that they "can see how the process works, so it's a little less mysterious than it was 10 years ago." Bradlee has noticed that many citizens are taking this process seriously and using his app to create legitimate redistricting maps that could be put in place. Some websites have called Bradlee the pioneer and cause of the rise of do-it-yourself redistricting. States such as Montana in 2021 allowed the general population to use it to submit redistricting proposals following the 2020 United States Census. Dave's Redistricting has frequently been mentioned as a resource that can be used to combat gerrymandering, given that the public has free access to it. Political science firms such as FiveThirtyEight have used the website to draw examples of gerrymandered districts, including on their famous Atlas of Redistricting. Dave Bradlee built the first generation of DRA. DRA 2020 is built by a small team of volunteers—Dave Bradlee, Terry Crowley, Alec Ramsay, and David Rinn—all with a shared passion for technology & democracy and all Microsoft veterans. Their mission is to empower civic organizations and citizen activists to advocate for fair congressional and legislative districts and increased transparency in the redistricting process. == Functions == Users can redraw the congressional and state legislative districts for all 50 states, the District of Columbia, and Puerto Rico using a variety of census and election datasets including Cook PVI. Maps can be optimized for different criteria. DRA 2020 added several major features to the first generation app: Sharing & collaborative editing of maps, like Google Docs Multiple statewide elections for all 50 states including the ability to import your own data Comprehensive analytics for evaluating and comparing maps Custom overlays, and Block-level editing DRA remains free to use. == Versions == 2.2: This uses Bing Maps, an outdated software that projects the districts of a single state onto a map of the United States. 2.5: After Bing Maps announced that it would no longer be updating for the foreseen future, the U.S. Map feature was removed. DRA 2020: At the end of 2018, a beta version of 2020 was released. This version that did not require Microsoft Silverlight and could be used in any web browser. DRA 2020 has been under continuous development since and is built using React (JavaScript library), Mapbox, OpenStreetMap, TypeScript, Node.js, Amazon Web Services, as well as many open source components, tools, and icons.

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  MetroHero

  MetroHero is a semi-defunct real-time transit tracking and performance analysis application for the Washington Metro rapid transit system. Originally available on iOS, Android, and the web, it allows users to view live maps of all trains on a specific line, summary statistics relating to real-time system performance, and user feedback on current Metro conditions. The app launched in 2015, followed by ARIES for Transit, a related project from the same developers, and continued functioning until its original developers shut it down in 2023. Afterwards, forks of the application went live to allow for its continued public use, and the Washington Metropolitan Area Transit Authority (WMATA), Metro's operator, announced that it would launch a similar app. The app has been described by local news media as popular and well-liked among Washington, D.C.-area residents. == History and main development == MetroHero was initially developed by James and Jennifer Pizzurro, who both attended George Washington University and studied computer science. They said that they were inspired to create the app after experiencing train delays and searching for an app to track a train after boarding; such an app did not exist for the Washington Metro. The development of the app was not endorsed by WMATA, but it did use publicly available data from the agency. MetroHero launched as an Android application in September 2015, followed by the release of an iOS-compatible web app in December of that year. A standalone iOS app launched in April 2018, but the web app remained supported. By April 2018, MetroHero had approximately 13,000 monthly active users. James Pizzurro has stated that the app's intended audience was regular Metro commuters who wanted to communicate with each other about active problems, as opposed to tourists and riders who only wanted train time data. Throughout the application's development, the Pizzurros had been advocates for Metro's transparency with riders and the community by providing more high-quality data and taking on the feedback of developers. In particular, they criticized Metro's reluctance to uniquely identify individual train trips and its decision to obscure data under certain circumstances, which have posed problems for MetroHero's data collection. In addition to their work on MetroHero, the app's developers led or participated in other initiatives related to transit in the Greater Washington area. In 2019, MetroHero partnered with a local transit group to analyze Metrobus data and publish a "Metrobus Report Card", along with proposed goals and recommendations based on the report's findings. Based on this experience, MetroHero's developers began a sister project, the Adherence + Reliability + Integrity Evaluation System for Transit (ARIES for Transit), which displays data and issues grades for Washington- and Baltimore-area transit systems. Separately, James Pizzurro used MetroHero data to inform Rail Transit OPS, an independent Metro oversight group, and assist in its documentation of Metro system incidents. == Application == The MetroHero application uses several interfaces, including an overall dashboard and a live map, to display data to its users. On the dashboard, system-wide train summary data, such as the number of operating trains and headway adherence, is visible. The map offers a visual representation of all trains' positions throughout the system, filtered by line. Individual stations and trains can be selected to see ratings and comments provided by other users, including both positive and negative notes like cleanliness and crowdedness. Additionally, a list of train wait times is given, along with aggregate data like average wait time. Any train delays or service incidents are visible in the app. MetroHero uses several data sources for the various components of its application. Train positions and other operational data are provided by WMATA as part of its initiative to release open data for third-party developers. However, MetroHero's developers noted that the Metro-provided information is sometimes inaccurate and incomplete, thereby limiting the accuracy of MetroHero. The app also collects crowdsourced data from its users, who can report conditions in train cars and stations and add to reports sent by other people. Additionally, MetroHero parses data from Twitter feeds to learn about system incidents, including delays and fires. In addition to the web app, Android app, and iOS app, MetroHero's initial developers maintained automated social media accounts that alerted customers about Metro service; these accounts were discontinued upon the original app's eventual shutdown. MetroHero also hosts archived performance data for later review, a feature that is sometimes used after major incidents. == Shutdown and future == In February 2023, James Pizzurro announced that MetroHero would be shut down on July 1, 2023, citing "positive changes ... in the app landscape and in WMATA's data management and communication" and the costs and time associated with maintaining the app. Shortly before the application's end date, the Pizzurros shared MetroHero's source code on GitHub, which prompted others to fork the code and begin maintaining new instances of MetroHero to succeed the original app. The original website went offline on July 1, as planned. Historically, WMATA has not offered its own real-time map or similar service, citing other apps from third parties which accomplished the same task. However, on June 30, 2023, Randy Clarke, WMATA's general manager, announced that Metro would begin offering a similar service as MetroHero did. The app, initially named MetroMeter, was planned to begin operating in early July and would provide real-time information on trains, headways, and service schedules. Metro also noted its intentions to extend this service to Metrobus and MetroAccess. On July 20, Metro announced that the app had been renamed to MetroPulse and launched it in beta. MetroHero's other project, ARIES for Transit, was not affected by the shutdown. == Reception == MetroHero was generally well-received and has been recognized for its usage among Washington-area commuters. DCist called it one of the "most praised" Metro tracking apps, and WMATA publicly acknowledged its popularity when announcing its decision to establish MetroPulse. Chris Barnes, a member of the Metro Riders' Advisory Council, said that the app is considered important among riders because it fulfills a need for riders to have reliable and transparent transit information, albeit somewhat hindered by flaws in WMATA's data.

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  Knowledge as a service

  Knowledge as a service (KaaS) is a computing service that delivers information to users, backed by a knowledge model, which might be drawn from a number of possible models based on decision trees, association rules, or neural networks. A knowledge as a service provider responds to knowledge requests from users through a centralised knowledge server, and provides an interface between users and data owners. KaaS is one of several cloud computing-dependent business models in which computer resources are sold on an on-demand and pay-as-you-use basis. == Overview == At the International Semantic Web Conference 2019, it was described how knowledge can be made live and evolve on the web allowing users to learn directly from elaborated knowledge, now appearing in the form of knowledge graphs. KaaS appear when knowledge graphs are accessed via services This is opposed to DaaS which might "compute large volumes of data; integrate and analyzes that data; and publish it in real-time, using Web service APIs" (from Data as a Service) where the KaaS is able to exploit context - both the context of the user in relation to their information requests of the KaaS (where and when they make the request) and also the context of the information in relation to some objective or purpose of the users either understood by the KaaS automatically or indicated to it by the user. == Differentiating knowledge from data == Conceptual models that make such a differentiation such as the so-called DIKW pyramid have existed for perhaps more than 40 years (see a 1974 journal article about this) however definitions are not stable and universally accepted (see the discussion about the conceptualizations of DIKW within the DIKW Wikipedia article that question value of wisdom). The knowledge component of DIKW is generally agreed to be an elusive concept which is difficult to define, however Rowley 2007, in a well known student textbook differentiated knowledge from data by stating that knowledge is "defined with reference to information" and that it contains more than just facts but also "beliefs and expectations". In relation to knowledge graphs, knowledge may be additional content they provide over and above pure data which is the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse (see the definition of Ontology). The ability to represent "beliefs and expectations", or other forms of not so straightforwardly explicit knowledge is an on-going area of improvement in information sciences (see Tacit knowledge) and, with relation to KaaS, the establishment of recent informatics mechanics to do so it critical to the legitimacy of KaaS as it is differentiated from just value-added DaaS. Knowledge graphs' ability to represent context via the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse that they provide (see the definition of Ontology) has led to the idea that supplying access to KNs might be a required competency of a KaaS. == Delivery of knowledge == Much service-delivered content is dependent on a session to provide much of the context that the user (client) needs to understand answers to questions. For example, using current HTTP internet protocols, a GET request to retrieve information identified by a URI, such as a web page, a client (a human or a machine) may have access information supplied automatically to enable that client to bypass paywalls or other content access controls. Such context, in this case about the client's information access allowances, can alter the information provided. In a logical extension to this internet protocols example, a server would receive from the client, either manually or automatically, a full context which would be information about the situation the client is in and this would allow the server to best interpret the client's request. Current internet protocols allow for formats, languages and related preferences to be expressed by clients but make no mention of what a client already knows and what they may understand. The recent Content Negotiation by Profile proposes additions to both the HTTP internet protocols and related services that allow clients to also request information - a response from the server - that accords with an identified information model. This then allows clients to indicate not just formats and languages that they understand (technically that they prefer) but also domains of discourse that that do, which is a step towards comprehensive client context provision.

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  Outline of brain mapping

  The following outline is provided as an overview of and topical guide to brain mapping: Brain mapping – set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. Brain mapping is further defined as the study of the anatomy and function of the brain and spinal cord through the use of imaging (including intra-operative, microscopic, endoscopic and multi-modality imaging), immunohistochemistry, molecular and optogenetics, stem cell and cellular biology, engineering (material, electrical and biomedical), neurophysiology and nanotechnology. == Broad scope == History of neuroscience History of neurology Brain mapping Human brain Neuroscience Nervous system. === The neuron doctrine === Neuron doctrine – A set of carefully constructed elementary set of observations regarding neurons. For more granularity, more current, and more advanced topics, see the cellular level section Asserts that neurons fall under the broader cell theory, which postulates: All living organisms are composed of one or more cells. The cell is the basic unit of structure, function, and organization in all organisms. All cells come from preexisting, living cells. The Neuron doctrine postulates several elementary aspects of neurons: The brain is made up of individual cells (neurons) that contain specialized features such as dendrites, a cell body, and an axon. Neurons are cells differentiable from other tissues in the body. Neurons differ in size, shape, and structure according to their location or functional specialization. Every neuron has a nucleus, which is the trophic center of the cell (The part which must have access to nutrition). If the cell is divided, only the portion containing the nucleus will survive. Nerve fibers are the result of cell processes and the outgrowths of nerve cells. (Several axons are bound together to form one nerve fibril. See also: Neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons. Neurons are generated by cell division. Neurons are connected by sites of contact and not via cytoplasmic continuity. (A cell membrane isolates the inside of the cell from its environment. Neurons do not communicate via direct cytoplasm to cytoplasm contact.) Law of dynamic polarization. Although the axon can conduct in both directions, in tissue there is a preferred direction of transmission from cell to cell. Elements added later to the initial Neuron doctrine A barrier to transmission exists at the site of contact between two neurons that may permit transmission. (Synapse) Unity of transmission. If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type. Dale's law, each nerve terminal releases a single type of neurotransmitter. Some of the basic postulates in the Neuron doctrine have been subsequently questioned, refuted, or updated. See the cellular level section topics for additional information. === Map, atlas, and database projects === Brain Activity Map Project – 2013 NIH $3 billion project to map every neuron in the human brain in ten years, based upon the Human Genome Project. NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative [1] Community outreach site for above where the public may comment [2] Human Brain Project (EU) – 1 billion euro, 10-year project to simulate the human brain with supercomputers. BigBrain A high-resolution 3D atlas of the human brain created as part of the HBP. Human Connectome Project – 2009 NIH $30 million project to build a network map of the human brain, including structural (anatomical) and functional elements. Emphasis included research into dyslexia, autism, Alzheimer's disease, and schizophrenia. See also Connectome a, comprehensive map of neural connections in the brain. Allen Brain Atlas – 2003 $100 million project funded by Paul Allen (Microsoft) BrainMaps – National Institute of Health (NIH) database including 60 terabytes of image scans of primate and non-primates, integrated with information covering structure and function. NeuroNames – Defines the brain in terms of about 550 primary structures (about 850 unique structures) to which all other structures, names, and synonyms are related. About 15,000 neuroanatomical terms are cross indexed, including many synonyms in seven languages. Coverage includes the brain and spinal cord of the four species most frequently studied by neuroscientists: human, macaque (monkey), rat and mouse. The controlled, standardized vocabulary for each structure is located in an unambiguous, strict physical hierarchy, and these terms are selected based on ease of pronunciation, mnemonic value, and frequency of use in recent neuroscientific publications. Relation of each structure to its superstructures and substructures is included. The controlled vocabulary is suitable for uniquely indexing neuroanatomical information in digital databases. Decade of the Brain 1990–1999 promotion by NIH and the Library of Congress "to enhance public awareness of the benefits to be derived from brain research". Communications targeted Members of Congress, staffs, and the general public to promote funding. Talairach Atlas see Jean Talairach Harvard Whole Brain Atlas see Human brain MNI Template see Medical image computing Blue Brain Project and Artificial brain International Consortium for Brain Mapping see Brain Mapping List of neuroscience databases NIH Toolbox National Institute of Health (USA) toolbox for the assessment of neurological and behavioral function Organization for Human Brain Mapping The Organization for Human Brain Mapping (OHBM) is an international society dedicated to using neuroimaging to discover the organization of the human brain. == Imaging and recording systems == This section covers imaging and recording systems. The general section covers history, neuroimaging, and techniques for mapping specific neural connections. The specific systems section covers the various specific technologies, including experimental and widely deployed imaging and recording systems. === General === Most imaging work to date on individual neurons has been conducted outside the brain, typically on large neurons, and has been most frequently destructive. New techniques are however rapidly emerging. Search on "Single neuron imaging" and see related topics: Biological neuron model, Single-unit recording, Neural oscillation, Computational neuroscience. dMRI (above) is also promising in non-destructive imaging of single neurons inside the brain. History of neuroimaging (redirects from Brain scanner) Neuroimaging (redirects from Brain function map) Connectomics – mapping technique showing neural connections in a nervous system. === Specific systems === Cortical stimulation mapping Diffusion MRI (dMRI) – includes diffusion tensor imaging (DTI) and diffusion functional MRI (DfMRI). dMRI is a recent breakthrough in brain mapping allowing the visualization of cross connections between different anatomical parts of the brain. It allows noninvasive imaging of white matter fiber structure and in addition to mapping can be useful in clinical observations of abnormalities, including damage from stroke. Electroencephalography (EEG) – uses electrodes on the scalp and other techniques to detect the electrical flow of currents. Electrocorticography – intracranial EEG, the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Electrophysiological techniques for clinical diagnosis Functional magnetic resonance imaging (fMRI) Medical image computing (brain research of leads medical and surgical uses of mapping technology) Neurostimulation (in research stimulation is frequently used in conjunction with imaging) Positron emission tomography (PET) – a nuclear medical imaging technique that produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Imaging and recording componentry === ==== Electrochemical ==== Haemodynamic response – the rapid delivery of blood to active neuronal tissues. Blood Oxygenation Level Dependent signal (BOLD), corresponds to the concentration of deoxyhemoglobin. The BOLD effect is based on the fact that when neuronal activity is increased in one part of the brain, there is also an increased amount of cerebral blood flow to that area. Functional m

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  Enonic XP

  Enonic XP is a free and open-source content platform. Developed by the Norwegian software company Enonic, the platform can be used to build websites, progressive web applications, or web-based APIs. Enonic XP uses an application framework for coding server logic with JavaScript, and has no need for SQL as it ships with an integrated content repository. The CMS is fully decoupled, meaning developers can create traditional websites and landing pages, or use XP in headless mode, that is without the presentation layer, for loading editorial content onto any device or client. Enonic is used by major organizations in Norway, including the national postal service Norway Post, the insurance company Gjensidige, the Norwegian Labour and Welfare Administration, and all the top football clubs in the national football league for men, Eliteserien. == Overview == Enonic XP ships with the content management system (CMS) Content Studio. This includes a visual drag and drop editor, a landing page editor, support for multi-site and multi-language, media and structured content, advanced image editing, responsive user interface, permissions and roles management, revision and version control, and bulk publishing. Integrations and applications can be directly installed via the "Applications" section in XP, where the platform finds apps approved in the official Enonic Market. There are no third-party databases in Enonic XP. Instead, the developers have built a distributed storage repository, avoiding the need to index content. The system brings together capabilities from Filesystem, NoSQL, document stores, and search in the storage technology, which automatically indexes everything put into the storage. Enonic XP supports deployment of server side JavaScript. The open-source framework runs on top of a JVM (Java virtual machine), and allows developers to run the same code in the browser and on the server, thus enabling them to employ JavaScript. While running on the Java virtual machine, Enonic XP can be deployed on most infrastructures. The dependency on a third-party application server to deploy code has been removed, as the platform is an application server by default. A developer can for instance insert his own modules and code straight into the system while it is running. JavaScript unifies all the technical elements, and Enonic XP features a MVC framework where everything on the back-end can be coded with server-side JavaScript. The Enonic platform can use any template engine. === Progressive web apps === Another feature of Enonic XP is the possibility for developers to create progressive web apps (PWA). A PWA is a web application that is a regular web page or website, but can appear to the user like a mobile application. === Headless CMS and integrations === Enonic XP is headless, which means it separates content and presentation. The platform supports GraphQL, provides several default APIs, and allows for building custom APIs through the Guillotine starter kit. Consequently, Enonic supports modern front-end frameworks, and offers integrations with e.g. Next.js and React. == History == Enonic AS was founded in 2000 by Morten Øien Eriksen and Thomas Sigdestad. The software company specialized in building services and solutions, including a content management system known as "Vertical Site", then "Enonic CMS". Being aware that they had application, database, and website teams working on separate silos toward the same goal, Enonic sought to combine the different elements into a single software. The resulting application platform Enonic XP, first released in 2015, includes a CMS as an optional surface layer. In March 2020, Enonic XP was ranked by SoftwareReviews, a division of Info-Tech Research Group, a Canadian IT research and analyst firm, as the "Leader" in Web Experience Management. The ranking is based on user reviews, and is featured in SoftwareReviews‘ Digital Experience Data Quadrant Report, a comprehensive evaluation and ranking of leading Web Experience Management vendors. Enonic was also ranked first in 2021 and 2022. === Release history === Enonic XP assumed the mantle from the previous content management system Enonic CMS, and thus began with "version 5.0.0." The following list only contains major releases. == Development and support == Enonic offers a user and developer community consisting of a forum, support system with tickets, documentation, codex, learning and training center with certifications, and various community groups. Writing about the support system, Mike Johnston of CMS Critic notes that "enterprise customers obviously get access to a higher level of personalized support, where the Enonic support team can respond as fast as two hours." The support system is divided in three levels: silver, gold and platinum—from next day business support to 24/7 support. As Enonic XP is open-source, known vulnerabilities, bugs and issues are listed on GitHub.

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  CloudHealth Technologies

  CloudHealth Technologies, now CloudHealth by VMware, is a software company based in Boston, Massachusetts. The company provides cloud computing services related to cost management, governance, automation, security, and performance. == History == CloudHealth Technologies was founded by Joe Kinsella in 2012. Dan Phillips joined as CEO and co-founder in late 2012, and Dave Eicher joined as co-Founder in January 2013. In May 2016, the company announced plans to expand from its Boston headquarters with branch offices in San Francisco, London, Washington, D.C., Sydney, Amsterdam, Tel Aviv, and Singapore. Headquarters moved in Boston from Fort Point to 100 Summer Street in the Spring of 2018, tripling in square footage. In September 2017, Tom Axbey—who was previously at Rave Mobile Safety—joined as the new CEO and President. VMware announced its intention to acquire CloudHealth Technologies on August 27, 2018. The acquisition is "part of the information technology company's continued push into cloud-based software services" according to Reuters. The deal closed on October 4, 2018, and was reported to be in excess of $500 million. == Technology == Delivered through a software as a service (SaaS) model, CloudHealth Technologies's platform collects and analyzes data from cloud computing services and other IT environments so clients can report on costs, inform their business models, and project future trends. CloudHealth Technologies is compatible with Amazon Web Services, Microsoft Azure, Google Cloud Platform, multicloud, and hybrid cloud environments. CloudHealth Technologies has received Amazon Web Services(AWS) Education Competency status, AWS Migration Competency status and achieved SOC 2 Type 2 Compliance. == Funding == As of June 2017, CloudHealth Technologies has raised a total of $85.7 million through four rounds of funding. In March 2013, CloudHealth Technologies announced that it had secured $4.5 million in Series A funding. This round was led by .406 Ventures and Sigma Prime Ventures. In January 2015, CloudHealth Technologies secured $12 million in Series B funding. This round was led by Scale Venture Partners, .406 Ventures, and Sigma Prime Ventures, and was followed by a $3.2 million extension round. In May 2016, CloudHealth Technologies announced $20 million in Series C funding, led by Sapphire Ventures, .406 Ventures, Scale Venture Partners and Sigma Prime Ventures. In June 2017, CloudHealth Technologies secured $46 million in Series D funding led by Kleiner Perkins Caufield & Byers with participation from Meritech Capital Partners, Sapphire Ventures, 406 Ventures, and Scale Venture Partners. == Competition == As of March 2023, CloudHealth Technologies competes with Cloudability by Apptio and CloudCheckr by NetApp.

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  Digital image correlation and tracking

  Digital image correlation and tracking is an optical method that employs tracking and image registration techniques for accurate 2D and 3D measurements of changes in 2D images or 3D volumes. This method is often used to measure full-field displacement and strains, and it is widely applied in many areas of science and engineering. Compared to strain gauges and extensometers, digital image correlation methods provide finer details about deformation, due to the ability to provide both local and average data. == Overview == Digital image correlation (DIC) techniques have been increasing in popularity, especially in micro- and nano-scale mechanical testing applications due to their relative ease of implementation and use. Advances in computer technology and digital cameras have been the enabling technologies for this method and while white-light optics has been the predominant approach, DIC can be and has been extended to almost any imaging technology. The concept of using cross-correlation to measure shifts in datasets has been known for a long time, and it has been applied to digital images since at least the early 1970s. The present-day applications are almost innumerable, including image analysis, image compression, velocimetry, and strain estimation. Much early work in DIC in the field of mechanics was led by researchers at the University of South Carolina in the early 1980s and has been optimized and improved in recent years. Commonly, DIC relies on finding the maximum of the correlation array between pixel intensity array subsets on two or more corresponding images, which gives the integer translational shift between them. It is also possible to estimate shifts to a finer resolution than the resolution of the original images, which is often called "sub-pixel" registration because the measured shift is smaller than an integer pixel unit. For sub-pixel interpolation of the shift, other methods do not simply maximize the correlation coefficient. An iterative approach can also be used to maximize the interpolated correlation coefficient by using non-linear optimization techniques. The non-linear optimization approach tends to be conceptually simpler and can handle large deformations more accurately, but as with most nonlinear optimization techniques, it is slower. The two-dimensional discrete cross correlation r i j {\displaystyle r_{ij}} can be defined in several ways, one possibility being: r i j = ∑ m ∑ n [ f ( m + i , n + j ) − f ¯ ] [ g ( m , n ) − g ¯ ] ∑ m ∑ n [ f ( m , n ) − f ¯ ] 2 ∑ m ∑ n [ g ( m , n ) − g ¯ ] 2 . {\displaystyle r_{ij}={\frac {\sum _{m}\sum _{n}[f(m+i,n+j)-{\bar {f}}][g(m,n)-{\bar {g}}]}{\sqrt {\sum _{m}\sum _{n}{[f(m,n)-{\bar {f}}]^{2}}\sum _{m}\sum _{n}{[g(m,n)-{\bar {g}}]^{2}}}}}.} Here f(m, n) is the pixel intensity or the gray-scale value at a point (m, n) in the original image, g(m, n) is the gray-scale value at a point (m, n) in the translated image, f ¯ {\displaystyle {\bar {f}}} and g ¯ {\displaystyle {\bar {g}}} are mean values of the intensity matrices f and g respectively. However, in practical applications, the correlation array is usually computed using Fourier-transform methods, since the fast Fourier transform is a much faster method than directly computing the correlation. F = F { f } , G = F { g } . {\displaystyle \mathbf {F} ={\mathcal {F}}\{f\},\quad \mathbf {G} ={\mathcal {F}}\{g\}.} Then taking the complex conjugate of the second result and multiplying the Fourier transforms together elementwise, we obtain the Fourier transform of the correlogram, R {\displaystyle \ R} : R = F ∘ G ∗ , {\displaystyle R=\mathbf {F} \circ \mathbf {G} ^{},} where ∘ {\displaystyle \circ } is the Hadamard product (entry-wise product). It is also fairly common to normalize the magnitudes to unity at this point, which results in a variation called phase correlation. Then the cross-correlation is obtained by applying the inverse Fourier transform: r = F − 1 { R } . {\displaystyle \ r={\mathcal {F}}^{-1}\{R\}.} At this point, the coordinates of the maximum of r i j {\displaystyle r_{ij}} give the integer shift: ( Δ x , Δ y ) = arg ⁡ max ( i , j ) { r } . {\displaystyle (\Delta x,\Delta y)=\arg \max _{(i,j)}\{r\}.} == Deformation mapping == For deformation mapping, the mapping function that relates the images can be derived from comparing a set of subwindow pairs over the whole images. (Figure 1). The coordinates or grid points (xi, yj) and (xi, yj) are related by the translations that occur between the two images. If the deformation is small and perpendicular to the optical axis of the camera, then the relation between (xi, yj) and (xi, yj) can be approximated by a 2D affine transformation such as: x ∗ = x + u + ∂ u ∂ x Δ x + ∂ u ∂ y Δ y , {\displaystyle x^{}=x+u+{\frac {\partial u}{\partial x}}\Delta x+{\frac {\partial u}{\partial y}}\Delta y,} y ∗ = y + v + ∂ v ∂ x Δ x + ∂ v ∂ y Δ y . {\displaystyle y^{}=y+v+{\frac {\partial v}{\partial x}}\Delta x+{\frac {\partial v}{\partial y}}\Delta y.} Here u and v are translations of the center of the sub-image in the X and Y directions respectively. The distances from the center of the sub-image to the point (x, y) are denoted by Δ x {\displaystyle \Delta x} and Δ y {\displaystyle \Delta y} . Thus, the correlation coefficient rij is a function of displacement components (u, v) and displacement gradients ∂ u ∂ x , ∂ u ∂ y , ∂ v ∂ x , ∂ v ∂ y . {\displaystyle {\frac {\partial u}{\partial x}},{\frac {\partial u}{\partial y}},{\frac {\partial v}{\partial x}},{\frac {\partial v}{\partial y}}.} DIC has proven to be very effective at mapping deformation in macroscopic mechanical testing, where the application of specular markers (e.g. paint, toner powder) or surface finishes from machining and polishing provide the needed contrast to correlate images well. However, these methods for applying surface contrast do not extend to the application of free-standing thin films for several reasons. First, vapor deposition at normal temperatures on semiconductor grade substrates results in mirror-finish quality films with RMS roughnesses that are typically on the order of several nanometers. No subsequent polishing or finishing steps are required, and unless electron imaging techniques are employed that can resolve microstructural features, the films do not possess enough useful surface contrast to adequately correlate images. Typically this challenge can be circumvented by applying paint that results in a random speckle pattern on the surface, although the large and turbulent forces resulting from either spraying or applying paint to the surface of a free-standing thin film are too high and would break the specimens. In addition, the sizes of individual paint particles are on the order of μms, while the film thickness is only several hundred nanometers, which would be analogous to supporting a large boulder on a thin sheet of paper. == Digital volume correlation == Digital Volume Correlation (DVC, and sometimes called Volumetric-DIC) extends the 2D-DIC algorithms into three dimensions to calculate the full-field 3D deformation from a pair of 3D images. This technique is distinct from 3D-DIC, which only calculates the 3D deformation of an exterior surface using conventional optical images. The DVC algorithm is able to track full-field displacement information in the form of voxels instead of pixels. The theory is similar to above except that another dimension is added: the z-dimension. The displacement is calculated from the correlation of 3D subsets of the reference and deformed volumetric images, which is analogous to the correlation of 2D subsets described above. DVC can be performed using volumetric image datasets. These images can be obtained using confocal microscopy, X-ray computed tomography, Magnetic Resonance Imaging or other techniques. Similar to the other DIC techniques, the images must exhibit a distinct, high-contrast 3D "speckle pattern" to ensure accurate displacement measurement. DVC was first developed in 1999 to study the deformation of trabecular bone using X-ray computed tomography images. Since then, applications of DVC have grown to include granular materials, metals, foams, composites and biological materials. To date it has been used with images acquired by MRI imaging, Computer Tomography (CT), micro-CT, confocal microscopy, and lightsheet microscopy. DVC is currently considered to be ideal in the research world for 3D quantification of local displacements, strains, and stress in biological specimens. It is preferred because of the non-invasiveness of the method over traditional experimental methods. Two of the key challenges are improving the speed and reliability of the DVC measurement. The 3D imaging techniques produce noisier images than conventional 2D optical images, which reduces the quality of the displacement measurement. Computational speed is restricted by the file sizes of 3D images, which are significantly larger than 2D images. For example, an

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  Bag-of-words model

  The bag-of-words (BoW) model is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. It has also been used for computer vision. An early reference to "bag of words" in a linguistic context can be found in Zellig Harris's 1954 article on Distributional Structure. == Definition == The following models a text document using bag-of-words. Here are two simple text documents: Based on these two text documents, a list is constructed as follows for each document: Representing each bag-of-words as a JSON object, and attributing to the respective JavaScript variable: Each key is the word, and each value is the number of occurrences of that word in the given text document. The order of elements is free, so, for example {"too":1,"Mary":1,"movies":2,"John":1,"watch":1,"likes":2,"to":1} is also equivalent to BoW1. It is also what we expect from a strict JSON object representation. Note: if another document is like a union of these two, its JavaScript representation will be: So, as we see in the bag algebra, the "union" of two documents in the bags-of-words representation is, formally, the disjoint union, summing the multiplicities of each element. === Word order === The BoW representation of a text removes all word ordering. For example, the BoW representation of "man bites dog" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple tasks that do not require word order. For instance, for document classification, if the words "stocks" "trade" "investors" appears multiple times, then the text is likely a financial report, even though it would be insufficient to distinguish between Yesterday, investors were rallying, but today, they are retreating.andYesterday, investors were retreating, but today, they are rallying.and so the BoW representation would be insufficient to determine the detailed meaning of the document. == Implementations == Implementations of the bag-of-words model might involve using frequencies of words in a document to represent its contents. The frequencies can be "normalized" by the inverse of document frequency, or tf–idf. Additionally, for the specific purpose of classification, supervised alternatives have been developed to account for the class label of a document. Lastly, binary (presence/absence or 1/0) weighting is used in place of frequencies for some problems (e.g., this option is implemented in the WEKA machine learning software system). == Hashing trick == A common alternative to using dictionaries is the hashing trick, where words are mapped directly to indices with a hash function. When using a hash function, no memory is required to store a dictionary. In practice, hashing simplifies the implementation of bag-of-words models and improves scalability. Collisions can occur when two words are hashed to the same index, but this happens infrequently and may function as a form of regularization.

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  GNU toolchain

  The GNU toolchain is a broad collection of programming tools produced by the GNU Project. These tools form a toolchain (a suite of tools used in a serial manner) used for developing software applications and operating systems. The GNU toolchain plays a vital role in development of Linux, some BSD systems, and software for embedded systems. Parts of the GNU toolchain are also directly used with or ported to other platforms such as Solaris, macOS, Microsoft Windows (via Cygwin and MinGW/MSYS/WSL2), Sony PlayStation Portable (used by PSP modding scene) and Sony PlayStation 3. == Components == Projects in the GNU toolchain are: GNU Autotools (build system) – Software build toolset from GNU GNU Binutils – GNU software development tools for executable code GNU Bison – Yacc-compatible parser generator program GNU C Library – GNU implementation of the standard C libraryPages displaying short descriptions of redirect targets GNU Compiler Collection – Free and open-source compiler for various programming languages GNU Debugger – Source-level debugger GNU m4 – General-purpose macro processor GNU make – Software build automation tool

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  Sunrise Calendar

  Sunrise is a discontinued electronic calendar application for mobile and desktop. The service was launched in 2013 by designers Pierre Valade and Jeremy Le Van. In October 2015, Microsoft announced that they had merged the Sunrise Calendar team into the larger Microsoft Outlook team where they will work closely with the Microsoft Outlook Mobile service. == History == The first iteration of Sunrise launched in 2012 and was a daily email digest of appointments, events and birthdays. Sunrise was launched initially as an iPhone application on February 19, 2013. In June 2013, Sunrise raised $2.2 million (~$2.91 million in 2024) in venture funding from Resolute.vc, NextView Ventures, Lerer Hippeau Ventures, SV Angel, and other angel investment firms like Loïc Le Meur, Dave Morin, Fabrice Grinda. In May 2014, Sunrise launched on Android as well as on the web via a web application. In July 2014, Sunrise announced it had raised $6 million (~$7.81 million in 2024) Series A from Balderton Capital. Bernard Liautaud joined the board. On February 11, 2015, Sunrise Atelier, Inc. was acquired by Microsoft for US$100 million (~$129 million in 2024). On October 28, 2015, Microsoft announced that Sunrise would be discontinued, and its functionality merged into Outlook Mobile. Microsoft later stated that the app would permanently cease functioning on August 31, 2016, but the shutdown was delayed to September 13, 2016, to coincide with an update to Outlook Mobile that incorporates aspects of Sunrise into its calendar interface. == Features == Sunrise allowed users to connect with Google Calendar, iCloud calendar and with Exchange Server. The following third-party services featured integration with Sunrise: Foursquare, GitHub, TripIt, Asana, Evernote, Google Tasks, Trello, Songkick, and Wunderlist. As a web app, users could sign-in and use Sunrise in a web browser, with no downloads required. A native Sunrise app could also be downloaded for OS X 10.9 and later, iOS 8.0 and later (both iPhone and iPad) as well as Android phones and tablets. In May 2015, Sunrise launched Meet, a keyboard for Android and iOS that lets users select available time slots in their calendar to schedule one-to-ones.

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