Encoding of probability distributions for Asymmetric Numeral Systems

Many data compressors regularly encode probability distributions for entropy coding - requiring minimal description length type of optimizations. Canonical prefix/Huffman coding usually just writes lengths of bit sequences, this way approximating probabilities with powers-of-2. Operating on more accurate probabilities usually allows for better compression ratios, and is possible e.g. using arithmetic coding and Asymmetric Numeral Systems family. Especially the multiplication-free tabled variant of the latter (tANS) builds automaton often replacing Huffman coding due to better compression at similar computational cost - e.g. in popular Facebook Zstandard and Apple LZFSE compressors. There is discussed encoding of probability distributions for such applications, especially using Pyramid Vector Quantizer(PVQ)-based approach with deformation, also tuned symbol spread for tANS.Comment: 5 pages, 4 figure

Lossless compression with latent variable models

We develop a simple and elegant method for lossless compression using latent variable models, which we call `bits back with asymmetric numeral systems' (BB-ANS). The method involves interleaving encode and decode steps, and achieves an optimal rate when compressing batches of data. We demonstrate it rstly on the MNIST test set, showing that state-of-the-art lossless compression is possible using a small variational autoencoder (VAE) model. We then make use of a novel empirical insight, that fully convolutional generative models, trained on small images, are able to generalize to images of arbitrary size, and extend BB-ANS to hierarchical latent variable models, enabling state-of-the-art lossless compression of full-size colour images from the ImageNet dataset. We describe `Craystack', a modular software framework which we have developed for rapid prototyping of compression using deep generative models

Fractal-cluster theory and thermodynamic principles of the control and analysis for the self-organizing systems

The theory of resource distribution in self-organizing systems on the basis of the fractal-cluster method has been presented. This theory consists of two parts: determined and probable. The first part includes the static and dynamic criteria, the fractal-cluster dynamic equations which are based on the fractal-cluster correlations and Fibonacci's range characteristics. The second part of the one includes the foundations of the probable characteristics of the fractal-cluster system. This part includes the dynamic equations of the probable evolution of these systems. By using the numerical researches of these equations for the stationary case the random state field of the one in the phase space of the $D$, $H$, $F$ criteria have been obtained. For the socio-economical and biological systems this theory has been tested.Comment: 37 pages, 20 figures, 4 table