Machine Analysis of Facial Expressions

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Computational Intelligence Sequential Monte Carlos for Recursive Bayesian Estimation

Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles with many particles with high inter-particle correlation. Frequent resampling, however, leads to a lack of diversity amongst the particle set in a problem known as sample impoverishment. Traditional sequential Monte Carlo methods attempt to resolve this correlated problem however introduce further data processing issues leading to minimal to comparable performance improvements over the sequential Monte Carlo particle filter. A new method, the adaptive path particle filter, is proposed for recursive Bayesian estimation of non-linear non-Gaussian dynamical systems. Our method addresses the weight degeneracy and sample impoverishment problem by embedding a computational intelligence step of adaptive path switching between generations based on maximal likelihood as a fitness function. Preliminary tests on a scalar estimation problem with non-linear non-Gaussian dynamics and a non-stationary observation model and the traditional univariate stochastic volatility problem are presented. Building on these preliminary results, we evaluate our adaptive path particle filter on the stochastic volatility estimation problem. We calibrate the Heston stochastic volatility model employing a Markov chain Monte Carlo on six securities. Finally, we investigate the efficacy of sequential Monte Carlos for recursive Bayesian estimation of astrophysical time series. We posit latent dynamics for both regularized and irregular astrophysical time series, calibrating fifty-five quasar time series using the CAR(1) model. We find the adaptive path particle filter to statistically significantly outperform the standard sequential importance resampling particle filter, the Markov chain Monte Carlo particle filter and, upon Heston model estimation, the particle learning algorithm particle filter. In addition, from our quasar MCMC calibration we find the characteristic timescale τ to be first-order stable in contradiction to the literature though indicative of a unified underlying structure. We offer detailed analysis throughout, and conclude with a discussion and suggestions for future work

Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

4D topology optimization: Integrated optimization of the structure and self-actuation of soft bodies for dynamic motions

Topology optimization is a powerful tool utilized in various fields for structural design. However, its application has primarily been restricted to static or passively moving objects, mainly focusing on hard materials with limited deformations and contact capabilities. Designing soft and actively moving objects, such as soft robots equipped with actuators, poses challenges due to simulating dynamics problems involving large deformations and intricate contact interactions. Moreover, the optimal structure depends on the object's motion, necessitating a simultaneous design approach. To address these challenges, we propose "4D topology optimization," an extension of density-based topology optimization that incorporates the time dimension. This enables the simultaneous optimization of both the structure and self-actuation of soft bodies for specific dynamic tasks. Our method utilizes multi-indexed and hierarchized density variables distributed over the spatiotemporal design domain, representing the material layout, actuator layout, and time-varying actuation. These variables are efficiently optimized using gradient-based methods. Forward and backward simulations of soft bodies are done using the material point method, a Lagrangian-Eulerian hybrid approach, implemented on a recent automatic differentiation framework. We present several numerical examples of self-actuating soft body designs aimed at achieving locomotion, posture control, and rotation tasks. The results demonstrate the effectiveness of our method in successfully designing soft bodies with complex structures and biomimetic movements, benefiting from its high degree of design freedom.Comment: 36 pages, 27 figures; for supplementary video, see https://youtu.be/sPY2jcAsNY