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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.
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 encoder part of the VAE takes an image as input and outputs a lower-dimensional latent representation of the image. This latent representation is then used as input to the U-Net. Once the model is trained, the encoder is used to encode images into latent representations, and the decoder is used to decode latent representations back into images.
The S bits from each constituent encoder are discarded. The parity bit may be used within another constituent code. In an example using the DVB-S2 rate 2/3 code the encoded block size is 64800 symbols (N=64800) with 43200 data bits (K=43200) and 21600 parity bits (M=21600).
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.
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:
Byte pair encoding [1] [2] (also known as BPE, or digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. [4] A slightly-modified version of the algorithm is used in large language model tokenizers.
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.