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The Â鶹´«Ã½AV Journal on Special Areas in Information Theory  (JSAIT) is a multi-disciplinary journal of special issues focusing on the intersections of information theory with fields such as machine learning, statistics, genomics, neuroscience, theoretical computer science, and physics. Any field that utilizes the fundamentals of information theory, including concepts such as entropy, compression, coding, mutual information, divergence, capacity, and rate distortion theory is a candidate for a JSAIT special issue.

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The Â鶹´«Ã½AV Journal on Selected Areas in Information Theory (JSAIT) seeks high quality technical papers on all aspects of Information Theory and its applications. JSAIT is a multi-disciplinary journal of special issues focusing on the intersections of information theory with fields such as machine learning, statistics, genomics, neuroscience, theoretical computer science, and physics.

Hypergraph-Based Source Codes for Function Computation Under Maximal Distortion

Submitted by admin on Wed, 10/23/2024 - 01:52

This work investigates functional source coding problems with maximal distortion, motivated by approximate function computation in many modern applications. The maximal distortion treats imprecise reconstruction of a function value as good as perfect computation if it deviates less than a tolerance level, while treating reconstruction that differs by more than that level as a failure.

Time-Invariant Prefix Coding for LQG Control

Submitted by admin on Wed, 10/23/2024 - 01:52

Motivated by control with communication constraints, in this work we develop a time-invariant data compression architecture for linear-quadratic-Gaussian (LQG) control with minimum bitrate prefix-free feedback. For any fixed control performance, the approach we propose nearly achieves known directed information (DI) lower bounds on the time-average expected codeword length. We refine the analysis of a classical achievability approach, which required quantized plant measurements to be encoded via a time-varying lossless source code.

Efficient Representation of Large-Alphabet Probability Distributions

Submitted by admin on Wed, 10/23/2024 - 01:52

A number of engineering and scientific problems require representing and manipulating probability distributions over large alphabets, which we may think of as long vectors of reals summing to 1. In some cases it is required to represent such a vector with only $b$ bits per entry. A natural choice is to partition the interval $[{0,1}]$ into $2^{b}$ uniform bins and quantize entries to each bin independently.

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution With Applications to Imaging Through Turbulence

Submitted by admin on Wed, 10/23/2024 - 01:52

We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN. Our approach requires no paired training data, adapts itself to the distribution of the turbulence, leverages domain-specific data priors, and can generalize from tens to thousands of measurements. We achieve such functionality through an adversarial sensing framework adapted from CryoGAN (Gupta et al. 2021), which uses a discriminator network to match the distributions of captured and simulated measurements.

Information Leakage in Index Coding With Sensitive and Nonsensitive Messages

Submitted by admin on Wed, 10/23/2024 - 01:52

Index coding can be viewed as a compression problem with multiple decoders with side information. In such a setup, an encoder compresses a number of messages into a common codeword such that every decoder can decode its requested messages with the help of knowing some other messages as side information. In this paper, we study how much information is leaked to a guessing adversary observing the codeword in index coding, where some messages in the system are sensitive and others are not.

A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs

Submitted by admin on Wed, 10/23/2024 - 01:52

Many modern applications involve accessing and processing graphical data, i.e., data that is naturally indexed by graphs. Examples come from Internet graphs, social networks, genomics and proteomics, and other sources. The typically large size of such data motivates seeking efficient ways for its compression and decompression. The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees.