We consider the one helper source coding problem posed and investigated by Ahlswede, Körner, and Wyner for a class of information sources with memory. For this class of information sources we give explicit inner and outer bounds of the admissible rate region. We also give a certain nontrivial class of information sources where the inner and outer bounds match.

In a spin chain governed by a local Hamiltonian, we consider a microcanonical ensemble in the middle of the energy spectrum and a contiguous subsystem whose length is a constant fraction of the system size. We prove that if the bandwidth of the ensemble is greater than a certain constant, then the average entanglement entropy (between the subsystem and the rest of the system) of eigenstates in the ensemble deviates from the maximum entropy by at least a positive constant.

The problem of statistical inference in its various forms has been the subject of decades-long extensive research. Most of the effort has been focused on characterizing the behavior as a function of the number of available samples, with far less attention given to the effect of memory limitations on performance. Recently, this latter topic has drawn much interest in the engineering and computer science literature.

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).

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?

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.