Modeling Individual Cyclic Variation in Human Behavior

Cycles are fundamental to human health and behavior. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov model method for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to model variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets -- of menstrual cycle symptoms and physical activity tracking data -- yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.Comment: Accepted at WWW 201

Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems

Many recommender systems are based on optimizing a linear weighting of different user behaviors, such as clicks, likes, shares, etc. Though the choice of weights can have a significant impact, there is little formal study or guidance on how to choose them. We analyze the optimal choice of weights from the perspectives of both users and content producers who strategically respond to the weights. We consider three aspects of user behavior: value-faithfulness (how well a behavior indicates whether the user values the content), strategy-robustness (how hard it is for producers to manipulate the behavior), and noisiness (how much estimation error there is in predicting the behavior). Our theoretical results show that for users, upweighting more value-faithful and less noisy behaviors leads to higher utility, while for producers, upweighting more value-faithful and strategy-robust behaviors leads to higher welfare (and the impact of noise is non-monotonic). Finally, we discuss how our results can help system designers select weights in practice

Factors Affecting Air Traffic Controller’s Weather Dissemination to Pilots

As the number of flights in the United States continues to rise steadily, an equally amplified need for reliability and safety has come to the forefront of aviation research. One of the most alarming trends is the number of general aviation (GA) accidents during severe weather events that occur yearly, with fatalities occurring in more than half of these cases. This study focuses on identifying factors influencing weather dissemination of Air Traffic Controllers (ATC) to GA pilots. Ten factors affecting controllers’ performance during severe weather events were identified through an in-depth literature review including controller mental workload, situation awareness, weather information format and accuracy, weather information needs, weather tool limitations, inaccurate assumption and bias, controller training and expereince, regulatory factor, supervisory factors, and pilot-controller relationship. Recommendation can be developed to address each factors so that aviation safety could be enhanced in severe weather situations

Design of Air Traffic Control Weather Related Training Program

Essential components of a new scenario-based air traffic control (ATC) training platform whose effectiveness is being analyzed are outlined with respect to its use in the decision-making skills of trainees when confronted with emergency situations. The custom designed platform allows the trainee to interact with the program such that the 10-minute ramification of a proposed aircraft redirection can be explored visually before a decision is made. Actual previous extreme weather incidences are used. Testing of the platform is ongoing with ATC students from Kent State University. Data from subjective pre- and postquestionnaires as well as objective decision parameters are currently being taken