A controlled environment for specimens in the electron microscope is produced within a space formed by two 20 mu m apertures 0.75 mm apart in the gap of the objective polepiece. An additional diffusion pump makes it possible to operate the microscope with gas at 1 atm pressure in the space without covering the apertures with membranes. A side entry stage permits rapid specimen changing and enables the whole of a normal grid to be scanned. Preliminary experiments using this system indicate that repeat periods less than 100 AA can be distinguished in wet catalase when helium pressure is used in the specimen chamber and the current density at the specimen is 0.1 nA mu m-2 or less. With higher beam currents, bubbling occurs in the specimen and the structure of the crystal is no longer observable.

In this paper, we consider the problem of providing robustness to adversarial communication in multi-agent systems. Specifically, we propose a solution towards robust cooperation, which enables the multi-agent system to maintain high performance in the presence of anonymous non-cooperative agents that communicate faulty, misleading or manipulative information. In pursuit of this goal, we propose a communication architecture based on Graph Neural Networks (GNNs), which is amenable to a novel Gaussian Process (GP)-based probabilistic model characterizing the mutual information between the simultaneous communications of different agents due to their physical proximity and relative position. This model allows agents to locally compute approximate posterior probabilities, or confidences, that any given one of their communication partners is being truthful. These confidences can be used as weights in a message filtering scheme, thereby suppressing the influence of suspicious communication on the receiving agent's decisions. In order to assess the efficacy of our method, we introduce a taxonomy of non-cooperative agents, which distinguishes them by the amount of information available to them. We demonstrate in two distinct experiments that our method performs well across this taxonomy, outperforming alternative methods. For all but the best informed adversaries, our filtering method is able to reduce the impact that noncooperative agents cause, reducing it to the point of negligibility, and with negligible cost to performance in the absence of adversaries.

Autonomous driving promises to transform road transport. Multivehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods-such as deep reinforcement learning-are emerging as a promising approach to automatically design intelligent driving policies that can cope with these challenges. Yet, the process of safely learning multi-vehicle driving behaviours is hard: while collisions-and their near-avoidance-are essential to the learning process, directly executing immature policies on autonomous vehicles raises considerable safety concerns. In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed. Key to our learning procedure is a sim2real approach that uses real-world online policy adaptation in a mixed-reality setup, where other vehicles and static obstacles exist in the virtual domain. This allows us to perform safe learning by simulating (and learning from) collisions between the learning agent(s) and other objects in virtual reality. Our results demonstrate that, after only a few runs in mixed-reality, collisions are significantly reduced.