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  2. Burst error-correcting code - Wikipedia

    en.wikipedia.org/wiki/Burst_error-correcting_code

    Proof. We need to prove that if you add a burst of length to a codeword (i.e. to a polynomial that is divisible by ()), then the result is not going to be a codeword (i.e. the corresponding polynomial is not divisible by ()).

  3. Boost (C++ libraries) - Wikipedia

    en.wikipedia.org/wiki/Boost_(C++_libraries)

    Boost is a set of libraries for the C++ programming language that provides support for tasks and structures such as linear algebra, pseudorandom number generation, multithreading, image processing, regular expressions, and unit testing. It contains 164 individual libraries (as of version 1.76).

  4. Uninitialized variable - Wikipedia

    en.wikipedia.org/wiki/Uninitialized_variable

    In the code snippet below, we have a struct student which contains some variables describing the information about a student. The function register_student leaks memory contents because it fails to fully initialize the members of struct student new_student. If we take a closer look, in the beginning, age, semester and student_number are ...

  5. Construct (Python library) - Wikipedia

    en.wikipedia.org/wiki/Construct_(Python_library)

    Construct is a Python library for the construction and deconstruction of data structures in a declarative fashion. In this context, construction, or building, refers to the process of converting (serializing) a programmatic object into a binary representation.

  6. Passive data structure - Wikipedia

    en.wikipedia.org/wiki/Passive_data_structure

    In Python, dataclass module provides dataclasses - often used as behaviourless containers for holding data, with options for data validation. The dataclasses in Python, introduced in version 3.7, that provide a convenient way to create a class and store data values. The data classes use to save our repetitive code and provide better readability ...

  7. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions. [11]

  8. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.

  9. Coroutine - Wikipedia

    en.wikipedia.org/wiki/Coroutine

    The library relies on Boost.Context and supports ARM, MIPS, PowerPC, SPARC and X86 on POSIX, Mac OS X and Windows. Boost.Coroutine2 - also created by Oliver Kowalke, is a modernized portable coroutine library since boost version 1.59. It takes advantage of C++11 features, but removes the support for symmetric coroutines.