Modern Rate-Distortion Theory (RDT) stands at the intersection of information theory, signal processing, and machine learning, offering a profound understanding of the tradeoff between data compression and reconstruction fidelity. This special issue aims to present the latest advancements in RDT, ranging from theoretical developments to practical applications across diverse domains. Topics include novel formulations of the rate-distortion tradeoff, deep learning approaches for optimization, applications in image and video compression, and extensions to non-standard data sources. By bringing together cutting-edge research and innovative methodologies, this special issue aims to shape the future landscape of RDT and its applications.

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.

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.

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.

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.