Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network

Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.Comment: Preprint for Journal Machine Learnin

Evaluation Of Pdms-Based Uv-Crosslinked Hydrogels Properties For Tissue Engineering Applications

This work presents the fabrication of PDMS-based hydrogels with tunable properties via direct blending. Two UV-crosslinkable PDMS with different molecular weights (Mn=1k & 6k g/mol) were first synthesized and then UV-cured with PEGDA (Mn=0.7k g/mol) at various wt.% ratio, in the presence of Irgacure as photoinitiator. For the medium Mn PDMS (6k), allyl methacrylate (AMA) was used as reactive modifier to enhance compatibility of the two highly immiscible polymers. The liquid mixtures were converted into hydrogels after exposed to UV irradiation at a wavelength region of 315-400 nm at the average intensity of 10 mW/cm2 for 30 minutes. Compatibility, thermal, swelling, wetting, mechanical, protein adsorption and cytotoxicity properties of these PDMS hydrogels were evaluated. From differential scanning calorimetry (DSC) study, although two Tg were observed in the hydrogels fabricated from the low Mn PDMS (1k), they were all compatible since the hydrogel surface was homogeneous at any PEG wt.% ratio, as supported by AFM result. The hydrogels fabricated from the PDMS (6k) were highly incompatible and this was especially the case for the 30 wt.% PEG with the occurrence of macrophase separation. This problem was solved with addition of AMA. The phase separation of these PDMS (6K) hydrogels affected other properties in which the more hydrophobic gel surface, after the addition of AMA, had lowered their swelling and wetting properties since there was a fewer amount of PEG domains to render the hydrophilic surface. Protein adsorption to these hydrogel was higher if the surface was dominated by the PDMS surfaces, yet the adsorption was still lower than the bare PDMS. Stiffness of the hydrogel was fall within an acceptable range of soft tissue at ~ 0.5-1 MPa, with the stiffness increased with the increased of PEG loading, and/or the decreased of AMA loading. Coupled with their non-cytotoxic property, the fabricated PDMS-based hydrogels could potentially be used as scaffolds for tissue engineering applications

Nonparametric Bayesian Topic Modelling with Auxiliary Data

The intent of this dissertation in computer science is to study topic models for text analytics. The first objective of this dissertation is to incorporate auxiliary information present in text corpora to improve topic modelling for natural language processing (NLP) applications. The second objective of this dissertation is to extend existing topic models to employ state-of-the-art nonparametric Bayesian techniques for better modelling of text data. In particular, this dissertation focusses on: - incorporating hashtags, mentions, emoticons, and target-opinion dependency present in tweets, together with an external sentiment lexicon, to perform opinion mining or sentiment analysis on products and services; - leveraging abstracts, titles, authors, keywords, categorical labels, and the citation network to perform bibliographic analysis on research publications, using a supervised or semi-supervised topic model; and - employing the hierarchical Pitman-Yor process (HPYP) and the Gaussian process (GP) to jointly model text, hashtags, authors, and the follower network in tweets for corpora exploration and summarisation. In addition, we provide a framework for implementing arbitrary HPYP topic models to ease the development of our proposed topic models, made possible by modularising the Pitman-Yor processes. Through extensive experiments and qualitative assessment, we find that topic models fit better to the data as we utilise more auxiliary information and by employing the Bayesian nonparametric method

Bayesian analysis of claim run-off triangles

This dissertation studies Markov chain Monte Carlo (MCMC) methods, and applies them to actuarial data, with a focus on claim run-off triangles. After reviewing a classical model for run-off triangles proposed by Hertig (1985) and improved by de Jong (2004), who incorporated a correlation structure, a Bayesian analogue is developed to model an actuarial dataset, with a view to estimating the total outstanding claim liabilities (also known as the required reserve). MCMC methods are used to solve the Bayesian model, estimate its parameters, make predictions, and assess the model itself. The resulting estimate of reserve is compared to estimates obtained using other methods, such as the chain-ladder method, a Bayesian over-dispersed Poisson model, and the classical development correlation model of de Jong. The thesis demonstrates that the proposed Bayesian correlation model performs well for claim reserving purposes. This model yields similar results to its classical counterparts, with relatively conservative point estimates. It also gives a better idea of the uncertainties involved in the estimation procedure

An improved image processing approach for machinery fault diagnosis

Wavelet analysis has been proven to be effective in analysing non-stationary vibration signals. However, the interpretation of the wavelet analysis results, such as a wavelet scalogram, requires high levels of knowledge and experience, which remains a great challenge to practitioners in the field. Recently, the rapid development and advancement of image processing technologies have shed new light on this challenge. In this study, image features such as Harris Stephens(Harris);speeded-up robust features(SURFs);and binary, robust, invariant, scalable keypoints (BRISKs)were obtained from a red, green, and blue (RGB) colour-filtered wavelet scalogram. Each colour filter generates a set of image features from an RGB-filtered wavelet scalogram. Then, the features were utilised as inputs to the fault classifier, namely the support vector machine (SVM),for fault classification. However, there will be a situation where the classification results from the fault classifier, based on the image generated from the different colour filters, are contradictory to each other. No conclusion can thus be made in these situations. This paper employed the Dempster-Shafer (DS) theory to refine the contradicting results and provide an ultimate conclusion to the machine condition. Therefore, the proposed method has improved the fault classification accuracy from 69% to 78%

Halted Lymphocyte Egress via Efferent Lymph Contributes to Lymph Node Hypertrophy During Hypercholesterolemia

Dyslipidemia is a central component of atherosclerosis and metabolic syndrome linked to chronic inflammation and immune dysfunction. Previously, we showed that hypercholesterolemic apolipoprotein E knock out (apoE−/−) mice exhibit systemic effects including skin inflammation and hypertrophic lymph nodes (LNs). However, the mechanisms accounting for LN hypertrophy in these mice remain unknown. Here, we show that hypercholesterolemia led to the accumulation of lymphocytes in LNs. We excluded that the increased number of lymphocytes in expanded LNs resulted from increased lymphocyte proliferation or entry into those LNs. Instead, we demonstrated that the egress of lymphocytes from the enlarged LN of apoE−/− mice was markedly decreased. Impairment in efferent lymphatic emigration of lymphocytes from LNs resulted from an aberrant expansion of cortical and medullary sinuses that became hyperplastic. Moreover, CCL21 was more abundant on these enlarged sinuses whereas lymph levels of sphingosine 1 phosphate (S1P) were decreased in apoE−/− mice. Normal LN size, lymphatic density and S1P levels were restored by reversing hypercholesterolemia. Thus, systemic changes in cholesterol can sequester lymphocytes in tissue draining LNs through the extensive remodeling of lymphatic sinuses and alteration of the balance between retention/egress signals leading to LN hypertrophy which subsequently may contribute to poor immunity. This study further illustrates the role of lymphatic vessels in immunity through the regulation of immune cell trafficking