We consider zero-delay joint source-channel coding of individual source sequences for a general known channel. Given an arbitrary finite set of schemes with finite-memory (not necessarily time-invariant) decoders, a scheme is devised that does essentially as well as the best in the set on all individual source sequences. Using this scheme, we construct a universal zero-delay joint source-channe...

If a message is sent in plain text, any single error may prevent us from recovering the original message. Such as sending cat and receiving rat . To circumvent this, redundant bits of information can be introduced to identify and correct errors. A simple example of encoding for this purpose would be to send each symbol thrice. To decode a received message (possibly with errors), one could look ...

Recently, Albanese et al. introduced priority encoding transmission (PET) for sending hierarchically organized messages over lossy packet-based computer networks [1]. In a PET system, each symbol in the message is assigned a priority which determines the minimal number of codeword symbols that is required to recover that symbol. This note revisits the PET approach using tools from network infor...

A fountain code produces for given set of k input symbols (x1, . . . , xk) a potentially limitless stream of output symbols z1, z2, . . .. The input and output symbols can be binary vectors of arbitrary length. Each output symbol is the sum of a randomly and independently chosen subset of the input symbols. Information describing the relations between input and output symbols is obtained at the...

We investigate the effect of symbolic encoding applied to times series consisting of some deterministic signal and additive noise, as well as time series given by a deterministic signal with randomly distributed initial conditions as a model of event-related brain potentials. We introduce an estimator of the signal-to-noise ratio (SNR) of the system by means of time averages of running complexi...

The deep connection between the Burrows-Wheeler transform (BWT) and the socalled rank and select data structures for symbol sequences is the basis of most successful approaches to compressed text indexing. Rank of a symbol at a given position equals the number of times the symbol appears in the corresponding prefix of the sequence. Select is the inverse, retrieving the positions of the symbol o...

This study evaluated the effect of sound-symbol association training on visual and phonological memory in children with a history of dyslexia. Pretests of phonological and visual memory, a sound-symbol training procedure, and phonological and visual memory posttests were administered to children with dyslexia, to children whose dyslexia had been compensated through remedial training, and to age...

We formulate the random coding exponent for single and multiple antennae systems on a flatfading additive white Gaussian noise (AWGN) channel with differential encoding and multiple-symbol differential detection (MSDD). Noting the intractability of the computation of the exponent for code lengths of interest, we propose the use of an approximated channel model based on the fact that the observa...

Applying machine learning to mathematical terms and formulas requires a suitable representation of that is adequate for AI methods. In this paper, we develop an encoding allows logical properties be preserved additionally reversible. This means the tree shape formula including all symbols can reconstructed from dense vector representation. We do by training two decoders: one extracts top symbol...

While for many years two alternative approaches to building intelligent systems, symbolic AI and neural networks, have each demonstrated specific advantages and also revealed specific weaknesses, in recent years a number of researchers have sought methods of combining the two into a unified methodology which embodies the benefits of each while attenuating the disadvantages. This work sets out t...