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The antifragile loves randomness and uncertainty, which also means crucially a love of errors, a certain class of errors.
The convolution code's structure is designed to produce distinct syndromes for a large class of errors appearing in the processing outputs.
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The BCH codes form a large class of error-correcting cyclic codes that are constructed using finite fields.
Towards this end, we propose a new class of error-correcting codes (HD-codes) with built-in security features that we use in the diffusion layer of the proposed cipher.
It is also asymptotically normal for a wide class of error distributions and can be used to model data with skewed distributions [ 42, 43].
This class of error has little effect on differences between groups within a single measurement period, but can cause apparent changes between measurement periods.
Unlike estimating a single error rate, estimating ɛ1 and ɛ2 is not a simple exercise in counting errors because when both repeat genotypes are error prone the class of error responsible for a mismatch can be ambiguous.
We will then have greater confidence in genotypes AB and CD than in CC, because only CC can have been affected by allelic dropout (assuming only one error per genotype), and attributing AB or CD to error would require us to invoke a less likely class of error.
It seems obvious that at the level at which individuals write new programs, a change of language can eliminate whole classes of errors, or make them possible.
A real convolution code in systematic form [16] is used to compute parity values associated with the processing outputs as shown in Figure 2. Certain classes of errors occurring anywhere in the overall system including the parity generation and regeneration subsystems are easily detected.
Each step in the algorithm was found to be useful in defeating a particular sub-class of errors in the phantom data set.
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