Open Access

Integration of molecular switching units into complex electronic circuits is considered to be the next step toward the realization of novel logic and memory devices. This paper reports on an ordered 2D network of neighboring ternary switching units represented by triazatruxene (TAT) molecules organized in a honeycomb lattice on a Ag(111) surface. Using low-temperature scanning tunneling microscopy, the bonding configurations of individual TAT molecules can be controlled, realizing up to 12 distinct states per molecule. The switching between those states shows a strong bias dependence ranging from tens of millivolts to volts. The low-bias switching behavior is explored in active units consisting of two and more interacting TAT molecules that are purposefully defined (programmed) by high-bias switching within the honeycomb lattice. Within such a unit the low-bias switching can be triggered and accessed by single-point measurements on a single TAT molecule, demonstrating up to 9 and 19 distinguishable states in a dyad and a tetrad of coupled molecules, respectively. High experimental control over the desired state, owing to bias-dependent hierarchical switching and pronounced switching directionality, as well as full reversibility, make this system particularly appealing, paving the way to design complex molecule-based memory systems.

A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.