Particle accelerators are indispensable tools in science, industry and health care. Almost all of them are based on microwave driving. Based on a similar principle, acceleration of electrons with the help of laser light has already been proposed decades ago: Nanophotonic structures are needed that generate an optical near-field mode efficiently propelling the electrons. We could recently demonstrate the accelerator on a chip.

The use of deep neural networks (DNNs) is currently transforming many areas of science and engineering. Although DNN-based techniques outperform traditional algorithms in most signal processing tasks, they can exhibit weaknesses such as reduced robustness and a tendency to produce hallucinations. These issues are linked to the DNN's Lipschitz constant, which typically worsens exponentially with the addition of layers. In this work, we present a framework for the design of stable networks with maximal expressivity.

The recent development of DNA-based data storage prototypes has raised several questions about how to optimally encode information in these systems. A distinguishing feature of this new storage paradigm is that the stored information is read via “shotgun” sequencing technologies. This means that the channel output comprises many short fragments of the input observed out of order. Motivated by this, we study the capacity of a class of “shuffling channels” that capture this inherent need to reorder the observed channel output.