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To convolutionally encode data, start with k memory registers, each holding one input bit.Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adders (a modulo 2 adder can be implemented with a single Boolean XOR gate, where the logic is: 0+0 = 0, 0+1 = 1, 1+0 = 1, 1+1 = 0), and n generator polynomials — one for each adder (see figure below).
The output of a priority encoder is the binary representation of the index of the most significant activated line. In contrast to the simple encoder, if two or more inputs to the priority encoder are active at the same time, the input having the highest priority will take precedence. It is an improvement on a simple encoder because it can ...
Fig 1 is an example of a SCCC. Fig. 1. SCCC Encoder. The example encoder is composed of a 16-state outer convolutional code and a 2-state inner convolutional code linked by an interleaver. The natural code rate of the configuration shown is 1/4, however, the inner and/or outer codes may be punctured to achieve higher code rates as needed.
A diagram of a sinusoidal positional encoding with parameters =, = A positional encoding is a fixed-size vector representation of the relative positions of tokens within a sequence: it provides the transformer model with information about where the words are in the input sequence.
The first diagram in this article shows three circles (one for each parity bit) and encloses data bits that each parity bit covers. The second diagram (shown to the right) is identical but, instead, the bit positions are marked. For the remainder of this section, the following 4 bits (shown as a column vector) will be used as a running example:
These examples show how various data sequences would be encoded by the COBS algorithm. In the examples, all bytes are expressed as hexadecimal values, and encoded data is shown with text formatting to illustrate various features: Bold indicates a data byte which has not been altered by encoding. All non-zero data bytes remain unaltered.
The encoder divides the current interval into sub-intervals, each representing a fraction of the current interval proportional to the probability of that symbol in the current context. Whichever interval corresponds to the actual symbol that is next to be encoded becomes the interval used in the next step. Example: for the four-symbol model above:
High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules: