Inconsistency of decision-making, the Achilles heel of referees

This is an accepted manuscript of an article published by Taylor & Francis in Journal of Sports Sciences on 12/12/2016, available online: https://doi.org/10.1080/02640414.2016.1265143 The accepted version of the publication may differ from the final published version.This study assessed whether decisions made by six qualified referees were consistent when watching the live 2016 televised Champions League Final. Referees were paired off into three separate rooms. Two referees watched the game with no supporters present. Two watched the game surrounded by Real Madrid supporters, and the remaining two watched the game surrounded by Athletic Madrid supporters. Referees were asked to decide whether each decision made by the on-field referee was either correct or incorrect. Results identified two types of refereeing inconsistency. The first type was a systematic tendency of the supporting crowds (both rooms) to influence the adjudicating referees to make fewer incorrect (disagree with the on-field referee) decisions (8 and 5) than referees in the “no supporters” room (19) (χ2 = 11.22 [df = 2], P = 0.004). The second type of inconsistency was the home advantage “bias”, where the surrounding crowd influenced the adjudicating referees to favour their team, by disagreeing with the decision made by the on-field referee (χ2 = 6.0 [df = 2], P = 0.0498). One explanation for these inconsistencies is that referees adopt a coping strategy of “avoidance”, i.e., when faced with difficult decisions, referees simply avoid making unpopular decisions by waving “play on”

Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Contributions to the Statistical Foundation of Data-driven Control

we demonstrate several techniques to prove safety guarantees for robust control problems with statistical structure; that is, for data-driven dynamical modeling or verification problems where uncertainty is modeled by probability. These guarantees are probabilistic in nature, in accordance with the statistical nature of the uncertainty, and can be derived with limited model assumptions. Indeed, some of the techniques require no more than measurability. We focus on two data-driven control problems: estimation of forward reachable sets from data, and robust control of time- and frequency-domain models defined by a Gaussian process regression model. In the former, we apply scenario optimization and statistical learning theory to obtain probabilistic guarantees of accuracy and confidence with minimal system knowledge. In the latter, we apply the theory of suprema of Gaussian processes to establish high-probability regions of attraction, L2 gain bounds, and integral quadratic constraints for the uncertain system

Optimization-based planning and control of AUVs applied to adaptive sampling under ice

This paper presents a framework for optimization-based informative planning and control with applications to adaptive sampling with AUVs under sea ice. A spatial model of the information of interest is approximated as a Gaussian process (GP), which is learned online from in-situ sensor data. The planner uses a two-layer model predictive control (MPC) scheme on a low-fidelity model of the vehicle for exploration and exploitation of the GP, subject to safety constraints. The planner trajectories are then tracked using a constant bearing based guidance law, aligning the desired orientation of the AUV toward the planned trajectory. The proposed framework enables the vehicle to plan and replan its mission as new data is obtained, while ensuring tracking of the planned trajectories and safety constraint satisfaction. Simulation results of a case study are presented for demonstrating the performance of the proposed method. An AUV is tasked with finding and tracking concentrations of marine biomass in 3D under sea ice while avoiding collisions