Conceptual design of the liquid metal laboratory of the TECHNOFUSION facility

The application of liquid metal technology in fusion devices requires R&D related to many phenomena: interaction between liquid metals and structural material as corrosion, erosion and passivation techniques; magneto-hydrodynamics; free surface fluid-dynamics and any other physical aspect that will be needed for their safe reliable operation. In particular, there is a significant shortage of experimental facilities dedicated to the development of the lithium technology. In the framework of the TECHNOFUSION project, an experimental laboratory devoted to the lithium technology development is proposed, in order to shed some light in the path to IFMIF and the design of chamber's first wall and divertors. The conceptual design foresee a development in two stages, the first one consisting on a material testing loop. The second stage proposes the construction of a mock-up of the IFMIF target that will allow to assess the behaviour of a free-surface lithium target under vacuum conditions. In this paper, such conceptual design is addressed

Exploring the Limitations of Behavior Cloning for Autonomous Driving

Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https://github.com/felipecode/coiltraine

Spectrum sharing and aggregation for future wireless networks, part II

The papers in this special issue represent the second one in the sequel of three special issues on spectrum sharing and aggregation for future wirelessn networks