This special issue of the IEEE Journal on Selected Areas in Information Theory is dedicated to the memory of Toby Berger, one of the most important information theorists of our time, who passed away in 2022 at the age of 81. He made foundational contributions to a wide range of areas in information theory, including rate-distortion theory, network information theory, quantum information theory, and bio-information theory. He also left a deep imprint on diverse fields in applied mathematics and theoretical engineering, such as Markov random fields, group testing, multiple access theory, and detection and estimation. Well known for his technical brilliance, he tackled many challenging problems, but above all, it is his pursuit of elegance in research and writing that shines throughout his work. The goal of this special issue is to celebrate Toby Berger’s lasting legacy and his impact on information theory and beyond. Original research papers on topics within the realm of his scientific investigations and their “offspring”, as well as expository articles that survey his pioneering contributions and their modern developments, are invited.

Submission deadline extended to December 15, 2023 (originally November 15, 2023)

Over the past decade, machine learning (ML), that is the process of enabling computing systems to take data and churn out decisions, has been enabling tremendously exciting technologies. Such technologies can assist humans in making a variety of decisions by processing complex data to identify patterns, detect anomalies, and make inferences. At the same time, these automated decision-making systems raise questions about security and privacy of user data that drive ML, fairness of the decisions, and reliability of automated systems to make complex decisions that can affect humans in significant ways. In short, how can ML models be deployed in a responsible and trustworthy manner that ensures fair and reliable decision-making? This requires ensuring that the entire ML pipeline assures security, reliability, robustness, fairness, and privacy. Information theory can shed light on each of these challenges by providing a rigorous framework to not only quantify these desirata but also rigorously evaluate and provide assurances. From its beginnings, information theory has been devoted to a theoretical understanding of the limits of engineered systems. As such, it is a vital tool in guiding machine learning advances.

We invite previously unpublished papers that contribute to the fundamentals, as well as the applications of information- and learning-theoretic methods for secure, robust, reliable, fair, private, and trustworthy machine learning. Exploration of such techniques to practical systems is also relevant.

Deadline: (October 2, 2023) further extended to October 29th, 2023.

To support the fast growth of IoT and cyber physical systems, as well as the advent of 6G, there is a need for communication and networking models that enable more efficient modes for machine-type communications. This calls for a departure from the assumptions of classical communication theoretic problem formulations as well as the traditional network layers. This new communication paradigm is referred to as goal or task oriented communication, or in a broader sense, is part of the emerging area of semantic communications.

Over the past decade, there have been a number of approaches towards novel performance metrics, starting from measures of timeliness such as the Age of Information (AoI), Query Age of Information (QAoI), to those that capture goal oriented nature, tracking or control performance such as Quality of Information (QoI), Value of Information (VoI) and Age of Incorrect Information (AoII), moving toward to more sophisticated end-to-end distortion metrics (e.g. MSE), ML performance, or human perception of the reproduced data, and the application of finite-blocklength information theory in the context of the remote monitoring of stochastic processes, and real-time control.

We invite original papers that contribute to the fundamentals, as well as the applications of semantic metrics, and protocols that use them, in IoT or automation scenarios.

Deadline: (March 2, 2023) extended to March 17, 2023.

This special issue of the IEEE Journal on Selected Areas in Information Theory is dedicated to the memory of Alexander Vardy, a pioneer in the theory and practice of channel coding. His ground-breaking contributions ranged from unexpected solutions of longstanding theoretical conjectures to ingenious decoding algorithms that broke seemingly insurmountable barriers to code performance. Inspired not just by the mathematical beauty of coding theory but also by its engineering utility, Alexander Vardy developed novel coding techniques that have had a profound impact on modern information technology, including computer memories, data storage systems, satellite communications, and wireless cellular networks. At the same time, his innovations left their imprint on other scientific disciplines, such as information theory, computer science, and discrete mathematics.

Deadline: (January 15, 2023) extended to January 29, 2023.

Causal determinism, is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. In recent years, many approaches to causal discovery have been proposed predominantly for two settings: a) for independent and identically distributed data and b) time series data. Furthermore, causality- inspired machine learning which harnesses ideas from causality to improve areas such as transfer learning, reinforcement learning, imitation learning, etc is attracting more and more interest in the research community. Yet fundamental problems in causal discovery such as how to deal with latent confounders, improve sample and computational complexity, and robustness remain open for the most part. This special issue aims at reporting progress in fundamental theoretical and algorithmic limits of causal discovery, impact of causal discovery on other machine learning tasks, and its applications in sciences and engineering.

Deadline: December 20, 2022. Extended to January 10, 2023

Modern networks rely on a variety of technologies to sense the environment for static or locomotive objects, in particular their shapes, distances, directions, or velocities. Sensing is a key feature in these networks and enables for example autonomous driving, motion sensing in health applications, target detection in smart cities, or optimal beam selections in millimeter wave communication. Besides these exciting new applications, sensing remains an important feature also for traditional applications such as temperature monitoring, or earthquake or fire detection, where new technologies are exploited including continuous feature monitoring over the entire range of an optical fiber network. The purpose of this special issue is to report on new exciting applications of sensing in modern networks, novel sensing architectures, innovative signal processing mechanisms related to sensing, as well as new results on the fundamental performance limits (resolution, sample complexity, robustness) of sensing systems. Particular focus will be on joint systems that integrate sensing with other tasks, for example communication, information retrieval (estimation, feature extraction, localization), super-resolution.

Deadline July 15, 2022 Extended to October 10, 2022

Deep learning methods have emerged as highly successful tools for solving inverse problems. They achieve state-of-the-art performance on tasks such as image denoising, inpainting, super-resolution, and compressive sensing. They are also starting to be used in inverse problems beyond imaging, including for solving inverse problems arising in communications, signal processing, and even on non-Euclidean data such as graphs. However, a wide range of important theoretical and practical questions remain unsolved or even completely open, including precise theoretical guarantees for signal recovery, robustness and out-of-distribution generalization, architecture design, and domain-specific applications and challenges. This special issue aims to advance cutting-edge research in this area, with an emphasis on its intersection with information theory.

Modern computation environments are struggling to store, communicate and process data in unprecedented volumes. These data, which come in new and evolving structures and formats, necessitate compression, lossless and lossy. Recent years have witnessed the emergence of new techniques, approaches, and modes for data compression. This special issue will focus on cutting edge research in this space.

Submission deadline: Extended to January 1, 2022. This special issue will focus on information theoretic aspects of distributed coding and computing. While applications and platforms such as distributed learning, cloud storage and computing, content delivery networks, and distributed ledgers are increasingly popular, there is a tremendous need to evaluate the fundamental limits of the existing solutions and develop efficient approaches to run them. This is particularly important considering the growing list of constrains and requirements in terms available resources, scalability, privacy, security, fault tolerance, speed, accuracy, and verifiability. In this context, information theory and coding can play a major role in expanding and employing various tools and techniques to deal with those challenging tasks. This special issue aims to attract contributions investigating the fundamental limits of distributed information systems and developing efficient coding techniques to meet those limits, satisfying the essential constraints.

Deadline Extended to September 15, 2021. This 8th special issue will focus on exploring how new advances in information theory can impact future communication systems. Next generation wireless networks will incorporate a large number of devices, dense and intelligent antenna arrays, and operate in higher frequencies. New task-aware communication modalities, such as sensing, learning and inference, will accelerate the shift from human-to-human to machine-to-machine type communications. Accordingly, communication systems will be designed with capacity, latency and accuracy in mind. Increasingly complex communication tasks will need to be carried out on devices with energy and hardware constraints, but will also be able to take advantage of in-network storage and computation.

The CFP for JSAIT's 7th Special Issue. The focus of this special issue is on the applications of coding to the broad area of networking for efficient exploitation and delivery of data. Various coding techniques have been devised to tackle erasures and achieve fundamental limits of compression to recover a message with a fidelity criterion. Motivated by the research in this direction and a wide variety of applications at the intersection of distributed systems and networking, this special issue will focus on key aspects ranging from employment of coding for enhancing the efficiency of networking, protocols, computation and delivery in distributed systems, to maintaining consistency in updates and improving accessibility in distributed storage systems, as well as providing desired performance tradeoffs in terms of efficiency, delay and atomicity. Manuscript deadline May 1, 2021 — Extended to May 15, 2021.

The CFP for JSAIT's 6th Special Issue. The intersection of information theory and computing has been a fertile ground for novel, relevant intellectual problems. Recently, coding-theoretic techniques have been designed to improve performance metrics in distributed computing systems. This has generated a significant amount of research that has produced novel fundamental limits, code deigns and practical implementations. The set of ideas leveraged by this new line of research is collectively referred to as coded computing. This special issue will focus on coded computing, including aspects such as tradeoffs between reliability, latency, privacy and security. Manuscript deadline February 15, 2021

Sequential methods underpin many of the most powerful learning techniques, such as reinforcement learning, multi-armed bandits, online convex optimization, and active learning. Although many practical algorithms have been developed for sequential learning, there is a strong need to develop theoretical foundations and to understand fundamental limits. Herein lies an excellent opportunity for information theory to provide answers given its vast arsenal of versatile techniques. At the same time, sequential learning has already started to motivate new problems and insights in information theory and has led to new perspectives. This special issue seeks to fertilize new topics at the intersection of information theory and sequential, active, and reinforcement learning, promoting synergy along the way. Paper deadline is October 1, 2020.

This special issue will focus on exploring the intersection of privacy and security with information and coding theory, as well as applications in communication theory, cryptography, computer science, machine learning, and hardware security. Paper deadline has been extended to August 15, 2020 (hard deadline)

This special issue will focus on the intersection of Information theory with estimation and inference. Information Theory has provided powerful tools as well as deep insights into optimal procedures for statistical inference and estimation. The application of these tools include characterization of optimal error probabilities in hypothesis testing, determination of minimax rates of convergence for estimation problems, analysis of message-passing and other efficient algorithms, as well as demonstrating the equivalence of different estimation problems. This issue will illuminate new connections between information theory, statistical inference, and estimation, as well as highlight applications where information-theoretic tools for inference and estimation have proved fruitful in a wide range of areas including signal processing, data mining, machine learning, pattern and image recognition, computational neuroscience, bioinformatics and cryptography.

This special issue will focus on Quantum Information Science, especially Communication and Computing. It will aim to bridge theory, practice and basic science, ranging from the state-of-the-art in quantum computing, to communication, coding, cryptography, and compression, as well as the current challenges remaining. An overarching theme is how the information-theoretic and quantum-physical perspectives complement each other in building these quantum technologies.

JSAIT's first special issue will focus on the mathematical foundations of deep learning as well as applications across information science.