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In this model, a transmitter transmits one of three symbols to two receivers. For two of the symbols, both receivers receive exactly what was sent; the third symbol, however, is received differently at each of the receivers. This is one of the simplest examples of a non-trivial capacity result for a non-stochastic channel.
In computing, a channel is a model for interprocess communication and synchronization via message passing. A message may be sent over a channel, and another process or thread is able to receive messages sent over a channel it has a reference to, as a stream. Different implementations of channels may be buffered or not, and either synchronous or ...
Neural Polar Decoders (NPDs) [14] are an advancement in channel coding that combine neural networks (NNs) with polar codes, providing unified decoding for channels with or without memory, without requiring an explicit channel model. They use four neural networks to approximate the functions of polar decoding: the embedding (E) NN, the check ...
Transaction requests take place by calling interface functions of these channel models, which encapsulate low-level details of the information exchange. At the transaction level, the emphasis is more on the functionality of the data transfers – what data are transferred to and from what locations – and less on their actual implementation ...
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In computer programming, a virtual method table (VMT), virtual function table, virtual call table, dispatch table, vtable, or vftable is a mechanism used in a programming language to support dynamic dispatch (or run-time method binding).
new control structures (example: looping constructs, branching constructs) scoping and binding constructs; simplified syntax for complex and repeated source code; top-level defining forms with compile-time side-effects; data-driven programming; embedded domain specific languages (examples: SQL, HTML, Prolog) implicit finalization forms
Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters (m ≥ n). It is used in some forms of nonlinear regression. The basis of the method is to approximate the model by a linear one and to refine the parameters by successive iterations.