This letter investigates the exploitation of an intelligent reflecting surface (IRS) to communicate securely in a two-way network consisting of an untrusted user. In particular, the transmit powers and the phase shift at each element of the IRS are optimized to maximize the sum-secrecy rate, such that the IRS-reflected and non-IRS-reflected signals are added destructively at the untrusted user. The proposed iterative algorithm converges rapidly to a feasible solution of high accuracy with a few iterations. Numerical results demonstrate sum-secrecy rate gains up to 120% compared to naive or partially optimized schemes.

We present MAGHair, a novel wearable technique that provides subtle haptic sensation by stimulating the body hair without touching the skin. Our approach builds on previous research in magnetic hair stimulation and magnetic locomotion. We use magnetic cosmetics to augment the body hair, which can then be stimulated by a wearable apparatus that combines electromagnets and permanent magnets. We provide technical insights on the implementation of a fully functional wrist-worn form factor and early adaptations into other form factors. In addition, we provide a workflow for evaluating and characterizing the magnetic cosmetic recipes. Finally, we evaluate MAGHair, which demonstrated that users could detect the sensation of hair movement that they described as gentle and unique.

This paper considers a two-player game where each player chooses a resource from a finite collection of options. Each resource brings a random reward. Both players have statistical information regarding the rewards of each resource. Additionally, there exists an information asymmetry where each player has knowledge of the reward realizations of different subsets of the resources. If both players choose the same resource, the reward is divided equally between them, whereas if they choose different resources, each player gains the full reward of the resource. We first implement the iterative best response algorithm to find an $ϵ$-approximate Nash equilibrium for this game. This method of finding a Nash equilibrium may not be desirable when players do not trust each other and place no assumptions on the incentives of the opponent. To handle this case, we solve the problem of maximizing the worst-case expected utility of the first player. The solution leads to counter-intuitive insights in certain special cases. To solve the general version of the problem, we develop an efficient algorithmic solution that combines online convex optimization and the drift-plus penalty technique.