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Epi Info is public domain statistical software for epidemiology developed by Centers for Disease Control and Prevention. [1]Spatiotemporal Epidemiological Modeler is a tool, originally developed at IBM Research, for modelings and visualizing the spread of infectious diseases.
This is a list of free and open-source software (FOSS) packages, computer software licensed under free software licenses and open-source licenses. Software that fits the Free Software Definition may be more appropriately called free software ; the GNU project in particular objects to their works being referred to as open-source . [ 1 ]
This is a list of models and meshes commonly used in 3D computer graphics for testing and demonstrating rendering algorithms and visual effects. Their use is important for comparing results, similar to the way standard test images are used in image processing .
COVID-19 simulation models are mathematical infectious disease models for the spread of COVID-19. [1] The list should not be confused with COVID-19 apps used mainly for digital contact tracing. Note that some of the applications listed are website-only models or simulators, and some of those rely on (or use) real-time data from other sources.
LaMDA, a family of conversational neural language models developed by Google. [61] LLaMA, a 2023 language model family developed by Meta that includes 7, 13, 33 and 65 billion parameter models. Mycroft, a free and open-source intelligent personal assistant that uses a natural language user interface. [62]
Multiplatform open source application for the solution of physical problems based on the Hermes library: University of West Bohemia: 3.2: 2014-03-03: GNU GPL: Free: Linux, Windows: CalculiX: It is an Open Source FEA project. The solver uses a partially compatible ABAQUS file format. The pre/post-processor generates input data for many FEA and ...
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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