Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL

Characterization of a RS-LiDAR for 3D Perception

High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.Comment: For ICRA201

Experimental and modeling study on the effect of opening location in the under-ventilated compartment fire

For compartment fires, a series of reduced-scale experiments and modeling were carried out in order to investigate how and why various opening location affects the compartment fire behaviors included: the temperature inside of the compartment (T_in), the external flame height (h_f), the heat release rate inside of the compartment (Q_in) and the heat release rate outside of the compartment (Q_ex). The existing equation showed fire behavior only depends on the opening area and height. The results were analyzed and compared to the current air mass inflow equation, and the results showed that various opening locations could influence the compartment fire behaviors. Two factors K and O were introduced to show that various opening locations can lead to different amounts of airflow into the compartment, and the different ratios of oxygen were consumed within total oxygen inflowed. This thesis contributed to the current knowledge of compartment fire's ventilation factor and can be applied to architecture design from a fire safety perspective

UNCERTAINTY MITIGATION IN IMAGE-BASED MACHINE LEARNING MODELS FOR PRECISION MEDICINE

Machine learning (ML) algorithms have been developed to build predictive models in medicine and healthcare. In most cases, the performance of ML models/algorithms is measured by predictive accuracy or accuracy-related measures only. In medicine, the model results are intended to guide physicians to make critical decisions regarding patient care. This means that quantifying and mitigating the uncertainty of the output is also very important as it will allow decision makers to know how much they can rely on the model output. My dissertation focuses on studying model uncertainty of image-based ML in the context of precision medicine of brain cancer. Specifically, I focus on developing ML models to predict intra-tumor heterogeneity of genomic and molecular markers based on multi-contrast magnetic resonance imaging (MRI) data for glioblastoma (GBM) – the most aggressive type of brain cancer. Intra-tumor heterogeneity has been found to be a leading cause of treatment failure of GBM. Devising a non-invasive approach to map out the molecular/genomic distribution using MRI helps develop treatment with high precision. My dissertation research addresses the model uncertainties due to high-dimensional and noisy features, sparsity of labeled data, and utility of domain knowledge. In the first study, we developed a Semi-supervised Gaussian Process with Uncertainty-minimizing Feature-selection (SGP-UF), which can incorporate selected unlabeled samples (i.e. unbiopsied regions of a tumor) in the model training, and integrate feature selection with a new criterion of seeking features that minimize the prediction uncertainty. In the second study, we developed a Knowledge-infused Global-Local data fusion (KGL) framework, which optimally fuses three sources of data/information including biopsy samples (labeled data, local/sparse), images (unlabeled data, global), and knowledge-driven mechanistic models. In the third study, we developed a Weakly Supervised Ordinal Support Vector Machine (WSO-SVM), which aims to leverage a combination of data sources including biopsy/labeled samples and unlabeled samples from the tumor and image data from the normal brain, as well as their intrinsic ordinal relationship. We demonstrate that these novel methods significantly reduce prediction uncertainty while at the same time achieving higher accuracy in precision medicine, which can inform personalized targeted treatment decisions that potentially improve clinical outcome.Ph.D

AN EMPIRICAL ANALYSIS OF STOCK MISPRICING: EVIDENCE FROM UK STOCK MARKET

In this dissertation, mispricing in the UK stock market is investigated. It is well documented that stock mispricing has been discovered in stock markets in the world. This paper first reviews the mispricing evidences, the models valuing stocks, and hypotheses explaining mispricing established by previous researchers. Then, a sample test is conducted to analyse the stock mispricing in UK stock market. A large panel of UK firms listed on London Stock Exchange has been used to accomplish the empirical examination. According to results of portfolio classification analysis and regressions, it is found that 1) firm characteristics measured by book-to-market ratio and firm size have a significantly negative/positive impact on mispricing of stock; 2) there‘s significantly positive relationship between the stock mispricing and the inflation; 3) stocks with high mispricing underperform stocks with low mispricing; 4) arbitrage opportunities and stock mispricing are positively related