We consider a line network of nodes, connected by additive white noise channels, equipped with local feedback. We study the velocity at which information spreads over this network. For transmission of a data packet, we give an explicit positive lower bound on the velocity, for any packet size. Furthermore, we consider streaming, that is, transmission of data packets generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity, and provide an explicit lower bound.

Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way correspondence between CPE loss GANs and f-GANs which minimize f-divergences. We also show that all symmetric f-divergences are equivalent in convergence.

Wireless communication links suffer from outage events caused by fading and interference. To facilitate a tractable analysis of network communication throughput and latency, we propose an outage link model to represent a communication link in the slow fading phenomenon. For a line-topology network with outage links, we study three types of intermediate network node schemes: random linear network coding, store-and-forward, and hop-by-hop retransmission. We provide the analytical formulas for the maximum throughputs and the end-to-end latency for each scheme.

Conventional image compression techniques are primarily developed for the human visual system. However, with the extensive use of deep neural networks (DNNs) for computer vision, more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we revisit the JPEG rate distortion theory for DNN vision. First, we propose a novel distortion measure, dubbed the sensitivity weighted error (SWE), for DNN vision.

This article addresses errors in [1]. Equation (2) contained an error wherein x was not bold. It is corrected below.

The problem of coding for the uplink and downlink of cloud radio access networks (C-RAN’s) with K users and L relays is considered. It is shown that low-complexity coding schemes that achieve any point in the rate-fronthaul region of joint coding and compression can be constructed starting from at most $4(K+L)-2$ point-to-point codes designed for symmetric channels. This reduces the seemingly hard task of constructing good codes for C-RAN’s to the much better understood task of finding good codes for single-user channels.

In this paper, the problem of zero-error network function computation is considered, where in a directed acyclic network, a single sink node is required to compute with zero error a function of the source messages that are separately generated by multiple source nodes. From the information-theoretic point of view, we are interested in the fundamental computing capacity, which is defined as the average number of times that the function can be computed with zero error for one use of the network.

The Shannon lower bound has been the subject of several important contributions by Berger. This paper surveys Shannon bounds on rate-distortion problems under mean-squared error distortion with a particular emphasis on Berger’s techniques. Moreover, as a new result, the Gray-Wyner network is added to the canon of settings for which such bounds are known. In the Shannon bounding technique, elegant lower bounds are expressed in terms of the source entropy power.

Proactive testing and interventions are crucial for disease containment during a pandemic until widespread vaccination is achieved. However, a key challenge remains: Can we accurately identify all new daily infections with only a fraction of tests needed compared to testing everyone, everyday?

Uniform continuity bounds on entropies are generally expressed in terms of a single distance measure between probability distributions or quantum states, typically, the total variation-or trace distance. However, if an additional distance measure is known, the continuity bounds can be significantly strengthened. Here, we prove a tight uniform continuity bound for the Shannon entropy in terms of both the local-and total variation distances, sharpening an inequality in I. Sason, IEEE Trans. Inf. Th., 59, 7118 (2013).