Continual machine learning for non-stationary data analysis

Although deep learning models have achieved significant successes in various fields, most of them have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning changes, a conventional machine learning model will learn and adapt to the new status. However, the model will not remember or recognise any revisits to the previous states. This causes performance reduction and re-training curves in dealing with periodic or irregularly reoccurring changes in the data or goals. Without continual learning ability, one cannot deploy an adaptive machine learning model in a changing environment. This thesis investigates the continual learning and mitigating the catastrophic forgetting problem in neural networks. We assume non-stationary data contains multiple different tasks which are coming in sequence and will not be stored. We propose a regularisation method, which is to identify and penalise the changes of important parameters of previous tasks while learning a new one. However, when the number of tasks is sufficiently large, this method cannot preserve all the previously learned knowledge, or it impedes the integration of new knowledge. This is also known as the stability-plasticity dilemma. To solve this problem, we proposed a replay method based on Generative Adversarial Networks (GANs). Different from other replay methods, the proposed model is not bounded by the fitting capacity of the generator. However, the number of parameters increases rapidly as the number of learned tasks grows. Therefore, we propose a continual learning model based on Bayesian neural networks and a Mixture of Experts (MoE) framework. The proposed model integrates different experts which are responsible for different tasks into a giant model. Previously knowledge is preserved, and new tasks can be efficiently learned by assigning new experts. Based on Monte-Carlo Sampling, the performance is not satisfied. To address this issue, we propose a Probabilistic Neural Network (PNN) and integrate it with a conventional neural network. The PNN can produce the likelihood given input and be used in a variety of fields. To apply continual learning methods to real-world applications, we then propose a semi-supervised learning model to analyse healthcare datasets. The proposed framework extracts the general features from unlabelled data. We integrate the PNN into the framework to classify the data, which includes a smaller set of labelled samples and continually learn the new cases. The proposed model has been tested on benchmark datasets and also a real-world clinical dataset. The results showed that our proposed model outperforms the state-of-the-art models without requiring prior knowledge of the tasks and overall accuracy of the continual learning. The experiments on the real-world clinical data were designed to identify the risk of Urinary Tract Infections (UTIs) using in-home monitoring data. The UTI risk analysis model has been deployed in a digital platform and is currently part of the on-going Minder clinical study at the UK Dementia Research Institute (UK DRI). An earlier version of the model was deployed as a part of a Class-I CE marked medical device. The UK DRI Minder platform and the deployed machine learning models, including the UTI risk analysis model developed in this research, are in the process to be accredited as a Class-IIa medical device. Overall, this PhD research tackles theoretical and applied challenges of continuous learning models in dealing with real-world data. We evaluate the proposed continual learning methods in a variety of benchmarks with comprehensive analysis and show their effectiveness. Furthermore, we have applied the proposed methods in real-world applications and demonstrated the applicability of the models to real-world settings and clinical problems.Open Acces

Domestic water pricing with household surveys : a study of acceptability and willingness to pay in Chongqing, China

In determining domestic water prices, policy makers often need to use information about the demand side rather than only relying on information about the supply side. Household surveys have frequently been employed to collect demand-side information. This paper presents a multiple bounded discrete choice household survey model. It discusses how the model can be utilized to collect and analyze information about the acceptability of different water prices by different types of households, as well as households'willingness to pay for water service improvement. The results obtained from these surveys can be directly utilized in the development of water pricing and subsidy policies. The paper also presents an empirical multiple bounded discrete choice study conducted in Chongqing, China. In this case, domestic water service quality was seriously inadequate, but financial resources were insufficient to improve service quality. With a survey of about 1,500 households in five suburban districts in Chongqing Municipality, this study shows that a significant increase in the water price is feasible as long as the poorest households can be properly subsidized and certain public awareness and accountability campaigns can be conducted to make the price increase more acceptable to the public. The analysis also indicates that the order in which hypothetical prices are presented to respondents systematically affects their answers, and should be taken into account when designing survey instruments.Town Water Supply and Sanitation,Water Supply and Sanitation Governance and Institutions,Environmental Economics&Policies,Water and Industry,Water Supply and Systems

‘Four S’: The Gist of Science Communication in Modern China

In modern China, the objectives of science communication and popularization can be classified into four broad categories: popularizing scientific knowledge, establishing scientific thought, advocating scientific method and promoting scientific spirit. Some refer to these as ‘Four S’, for short. The ‘Four S’ (scientific knowledge, scientific method, scientific thought, scientific spirit) embodies the characteristics of contemporary Chinese science communication and popularization, which also largely stems from the context of science entering into and spreading throughout China since the late 19th century. At the same time, the ‘Four S’ encounters a few challenges in the ever-changing environment. It is argued that scientific thought and scientific spirit will determine the direction of science communication and popularization in the future, and thus also regulate the relationship between science and society in China

Review of the 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020

The 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020 was organised by the China Research Institute for Science Popularization (CRISP) in Beijing from October 17 to October 18, 2015. Nearly 200 international and national delegates from scientific research institutions, colleges and universities, local associations for science and technology from eight countries including America, Canada, Sweden, Australia, New Zealand, India, Japan and Korea participated in the Conference

The construction and practice path of safety education mechanism in colleges and universities integrating the psychological characteristics of students in the new era

BackgroundWith the rapid development of higher education in China, the scale of colleges and universities is expanding, and the phenomenon of campus socialization is becoming more and more obvious. In particular, the campus and its surrounding environment are becoming more and more complex, which brings many hidden dangers in university life.ObjectiveIn order to improve the effectiveness of safety education in colleges and universities and maintain the long-term effectiveness of college students’ safety awareness, the paper proposes the construction and practice path of college safety education mechanism that integrates the psychological characteristics of students in the new era.MethodsSecurity issues facing universities at home, this track identifies the relationship between campus security incidents and security education and advocacy. Eight solutions to prevent and reduce incidents in schools. The paper proposes to give importance to the study of the security of college students, to create an awareness of security questions in the bank based on the recommendation algorithm, and to create to have online learning and testing for safety awareness.ResultsThe passing rate of 10 majors such as humanities, composition and theory of composition technology was 100%, accounting for 12% of the 83 enrolled majors, and the passing rate of 54 majors such as clinical medicine was over 90%.ConclusionThe safety online learning and testing system of college students’ safety education is lively in form and highly accepted by students. The development of college students’ safety education starts from the time of receiving the university admission notice, making full use of the “golden time,” so as to effectively prevent and reduce the occurrence of campus safety accidents

Medical Image Registration Framework Using Multiscale Edge Information

AbstractEfficient multiscale deformable registration frameworks are proposed by combining edge preserving scale space (EPSS) with the free form deformation (FFD) for registration of medical images, where multiscale edge information can be used for optimizing the registration process. EPSS which is derived from the total variation model with the L1 norm (TV-L1) can provide useful spatial edge information for mutual information (MI) based registration. At each scale in registration process, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, and then the deformation fields can be gained by a coarse to fine manner. In our deformable registration framework, two ways are proposed for implementing this idea. The experiments on clinical images including PETCT and CT-CBCT show accuracy and robustness when compared to traditional method for medical imaging system

Discrimination of Colon Cancer Stem Cells Using Noncanonical Amino Acid

Cancer stem cells (CSCs) may be responsible for tumor recurrence. Metabolic labelling of newly synthesized proteins with non-canonical amino acids allows us to discriminate CSCs in mixed populations due to the quiescent nature of these cells

Review of National Conference on the Theoretical Study of Science Popularization: Theoretical and Practical Studies of Science Popularization

Ever since modern science was introduced into China, Chinese progressive intellectuals began to actively communicate science, hoping to raise and improve the vision and quality of Chinese society. With the constant development of China’s popular science career and innovating popular science practices, the demand for theoretical research in science popularization is constantly increasing. Meanwhile, the abundant practice and study has formed a certain paradox with the research on science communication lagging behind. Thus, the need of the hour is to have a strong base for theoretical research (Chuanhong, 2010)