Neurostimulation, doping, and the spirit of sport

There is increasing interest in using neuro-stimulation devices to achieve an ergogenic effect in elite athletes. Although the World Anti-Doping Authority (WADA) does not currently prohibit neuro-stimulation techniques, a number of researchers have called on WADA to consider its position on this issue. Focusing on trans-cranial direct current stimulation (tDCS) as a case study of an imminent so-called ‘neuro-doping’ intervention, we argue that the emerging evidence suggests that tDCS may meet WADA’s own criteria (pertaining to safety, performance-enhancing effect, and incompatibility with the ‘spirit of sport’) for a method’s inclusion on its list of prohibited substances and methods. We begin by surveying WADA’s general approach to doping, and highlight important limitations to the current evidence base regarding the performance-enhancing effect of pharmacological doping substances. We then review the current evidence base for the safety and efficacy of tDCS, and argue that despite significant shortcomings, it may be sufficient for WADA to consider prohibiting tDCS, in light of the comparable flaws in the evidence base for pharmacological doping substances. In the second half of the paper, we argue that the question of whether WADA ought to ban tDCS turns significantly on the question of whether it is compatible with the ‘spirit of sport’ criterion. We critique some of the previously published positions on this, and advocate our own sport-specific and application-specific approach. Despite these arguments, we finally conclude by suggesting that tDCS ought to be monitored rather than prohibited due to compelling non-ideal considerations

A review of deep learning methods and applications for unmanned aerial vehicles

Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.This work was supported by the Spanish Ministry of Science (Project DPI2014-60139-R). The LAL UPM and the MONCLOA Campus of International Excellence are also acknowledged for funding the predoctoral contract of one of the authors.Peer Reviewe