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PDFsharp is an open source [1].NET library for processing PDF files. It is written in C#.The library can be used to create, render, print, split, merge, modify, and extract text and meta-data of PDF files.
Razor is an ASP.NET programming syntax used to create dynamic web pages with the C# or VB.NET programming languages. Razor was in development in June 2010 [4] and was released for Microsoft Visual Studio 2010 in January 2011. [5] Razor is a simple-syntax view engine and was released as part of MVC 3 and the WebMatrix tool set. [5]
These text files can ultimately be any text format, such as code (for example C#), XML, HTML or XAML. T4 uses a custom template format which can contain .NET code and string literals in it, this is parsed by the T4 command line tool into .NET code, compiled and executed. The output of the executed code is the text file generated by the template ...
Dapr (Distributed Application Runtime) is a free and open source runtime system designed to support cloud native and serverless computing. [2] Its initial release supported SDKs and APIs for Java, .NET, Python, and Go, and targeted the Kubernetes cloud deployment system. [3] [4] The source code is written in the Go programming language.
Four years later, in 2004, a free and open-source project called Microsoft Mono began, providing a cross-platform compiler and runtime environment for the C# programming language. A decade later, Microsoft released Visual Studio Code (code editor), Roslyn (compiler), and the unified .NET platform (software framework), all of which support C# ...
At the Microsoft Connect event on December 4, 2018, Microsoft announced releasing WPF as open source project on GitHub. It is released under the MIT License. Windows Presentation Foundation has become available for projects targeting the .NET software framework, however, the system is not cross-platform and is still available only on Windows ...
dotnet.github.io /infer / Infer.NET is a free and open source .NET software library for machine learning . [ 2 ] It supports running Bayesian inference in graphical models and can also be used for probabilistic programming .
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.