Transforming Competency and Skill Development

Work Package 2

Aim of Work Package

This work package will deploy technology to assist in manpower training in ways that are not currently adopted in the aviation industry.  Specifically, the projects will use eye tracking technology to quantify and personalize training programs for pilots, and use augmented reality (AR) and virtual reality (VR) technology to complement existing training programs and to provide SIA with greater learning flexibility. 

Publications

Bayesian Herd Detection for Dynamic Data

Jussi Keppo, Ville Satopää

Problem Statement: This article analyzes multiple agents who forecast an underlying dynamic state based on streams of (partially overlapping) information. Each agent minimizes a convex combination of their forecasting error and deviation from the other agents' forecasts. As a result, the agents exhibit herding behavior - a bias that has been well-recognized in the economics and psychology literature. Our first contribution is a general framework for analyzing agents' forecasts under different levels of herding. The underlying state dynamics can be non-linear with seasonality, trends, shocks, or other time-series components....

This research is supported by National Research Foundation, Singapore and A*STAR, under its RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) grant call (Grant No. I2001E0059)

DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes

Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou

Problem Statement: Modeling dynamic scenes is important for many applications such as virtual reality and telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic scenes, existing methods based on Neural Radiance Fields (NeRF) suffer from slow convergence (i.e., model training time measured in days). In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields.....

This research is supported by National Research Foundation, Singapore and A*STAR, under its RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) grant call (Grant No. I2001E0059)

Incentive Design and Pricing under Limited Inventory

Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou

Problem Statement: A firm faces random demand for a service it delivers on a given future date. To boost demand, the firm hires a sales agent who exerts unobservable effort continuously over time. The firm is concerned not only with increasing current demand, but also with smoothing demand over time to avoid losing goodwill if realized demand exceeds available inventory. We model the firm’s incentive design problem using a continuous-time principal-agent framework, in which demand drifts over time in response to unobserved agent effort and the price the firm charges. To induce the agent’s sales effort, the firm chooses an incentive scheme that depends on the remaining inventory and the time to the service (e.g., time to departure in the case of airlines). We characterize the firm’s optimal incentive scheme .....

This research is supported by National Research Foundation, Singapore and A*STAR, under its RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) grant call (Grant No. I2001E0059)