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  2. Galois/Counter Mode - Wikipedia

    en.wikipedia.org/wiki/Galois/Counter_Mode

    In cryptography, Galois/Counter Mode (GCM) [1] is a mode of operation for symmetric-key cryptographic block ciphers which is widely adopted for its performance. GCM throughput rates for state-of-the-art, high-speed communication channels can be achieved with inexpensive hardware resources.

  3. Albumin transport function analysis by EPR spectroscopy

    en.wikipedia.org/wiki/Albumin_transport_function...

    The test is based on the interaction of spin-labeled fatty acids with serum albumin by means of EPR spectroscopy. [1] [2] [3] A sample of blood serum is subjected to a spin probe 16-doxyl stearate. Spin probe molecules bind specifically to albumin molecules where they occupy two main positions:

  4. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    This page was last edited on 26 October 2024, at 11:02 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  5. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.

  6. De novo sequence assemblers - Wikipedia

    en.wikipedia.org/wiki/De_novo_sequence_assemblers

    The authors make several suggestions for assembly: 1) use more than one assembler, 2) use more than one metric for evaluation, 3) select an assembler that excels in metrics of more interest (e.g., N50, coverage), 4) low N50s or assembly sizes may not be concerning, depending on user needs, and 5) assess the levels of heterozygosity in the ...

  7. Structural risk minimization - Wikipedia

    en.wikipedia.org/wiki/Structural_risk_minimization

    The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data. This principle was first set out in a 1974 book [1] by Vladimir Vapnik and Alexey Chervonenkis and uses the VC dimension.

  8. MTD(f) - Wikipedia

    en.wikipedia.org/wiki/MTD(f)

    MTD(f) is a shortened form of MTD(n,f) which stands for Memory-enhanced Test Driver with node ‘n’ and value ‘f’. [1] The efficacy of this paradigm depends on a good initial guess, and the supposition that the final minimax value lies in a narrow window around the guess (which becomes an upper/lower bound for the search from root).

  9. Rate-monotonic scheduling - Wikipedia

    en.wikipedia.org/wiki/Rate-monotonic_scheduling

    Liu and Layland noted that this bound may be relaxed to the maximum possible value of 1.0, if for tasks , where > and =, is an integer multiple of , which is to say that all tasks have a period that is not just a multiple of the shortest period, , but instead that any task's period is a multiple of all shorter periods.