Bayesian Semi-supervised Learning with Graph Gaussian Processes

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv version contains two identical sub-figure

Learning patterns from sequential and network data using probabilistic models

The focus of this thesis is on developing probabilistic models for data observed over temporal and graph domains, and the corresponding variational inference algorithms. In many real-world phenomena, sequential data points that are observed closer in time often exhibit higher degrees of dependency. Similarly, data points observed over a graph domain (e.g., user interests in a social network) may exhibit higher dependencies with lower degrees of separation over the graph. Furthermore, the connectivity structures that define the graph domain can also evolve temporally (i.e., temporal networks) and exhibit dependencies over time. The data sets observed over temporal and graph domains often (but not always) violate the independent and identically distributed (i.i.d.) assumption made by many mathematical models. The works presented in this dissertation address various challenges in modelling data sets that exhibit dependencies over temporal and graph domains. In Chapter 3, I present a stochastic variational inference algorithm that enables factorial hidden Markov models for sequential data to scale up to extremely long sequences. In Chapter 4, I propose a simple but powerful Gaussian process model that captures the dependencies of data points observed on a graph domain, and demonstrate its viability in graph-based semi-supervised learning problems. In Chapter 5, I present a dynamical model for graphs that captures the temporal evolution of the connectivity structures as well as the sparse connectivity structures often observed in temporal real network data sets. Finally, I summarise the contributions of the thesis and propose several directions for future works that can build on the proposed methods in Chapter 6

Bis(μ-3-nitro­phthalato-κ2 O 1:O 2)bis­[aqua­(2,2′-bipyridine-κ2 N,N′)copper(II)] dihydrate

Two 3-nitro­phthalate dianions bridge two water-coordinated 2,2′-bipyridine-chelated CuII atoms about a center of inversion to generate the title dinuclear compound, [Cu2(C8H3NO6)2(C10H8N2)2(H2O)2]·2H2O. The geometry of the CuII atom is a distorted square pyramid. Adjacent mol­ecules are linked through the coordinated and solvent water mol­ecules to form a linear ribbon running along the a axis of the monoclinic unit cell

Transductive Kernels for Gaussian Processes on Graphs

Kernels on graphs have had limited options for node-level problems. To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning. The kernel is derived from a regularization framework by treating the graph and feature data as two Hilbert spaces. We also show how numerous kernel-based models on graphs are instances of our design. A kernel defined this way has transductive properties, and this leads to improved ability to learn on fewer training points, as well as better handling of highly non-Euclidean data. We demonstrate these advantages using synthetic data where the distribution of the whole graph can inform the pattern of the labels. Finally, by utilizing a flexible polynomial of the graph Laplacian within the kernel, the model also performed effectively in semi-supervised classification on graphs of various levels of homophily

Herba Epimedii: Anti-oxidative properties and its medical implications

Herba Epimedii is a Chinese herbal medicine with proven efficacy in treating cardiovascular diseases and osteoporosis, and in improving sexual and neurological functions. This efficacy is found to be related to the potent anti-oxidative ability of Herba Epimedii and its flavonoid components, with icarrin as the main effective constituent, along with polysaccharides and vitamin C. These ingredients have been proven to be effective against oxidative-stress related pathologies (cardiovascular diseases, Alzheimer's disease and inflammation) in animal rodent models and in vitro studies. Their antioxidative properties are found to be related to an inductive effect on endogenous freeradical scavenging enzymes such as catalase and glutathione peroxidase and the inherent electron-donating ability of flavonoids. © 2010 licensee MDPI, Basel, Switzerland.published_or_final_versio

Patient, staff empowerment and hand hygiene bundle improved and sustained hand hygiene in hospital wards

AIM: We piloted a hand hygiene (HH) project in a ward, focusing on World Health Organization moments 1 and 4. Our aim was to design highly reliable interventions to achieve &gt;90% compliance.METHODS: Baseline HH compliance was 57 and 67% for moments 1, 4, respectively, in 2015. After the pilot ward showed sustained improvement, we launched the 'HH bundle' throughout the hospital. This included: (i) appointment of HH champions; (ii) verbal/visual bedside reminders; (iii) patient empowerment; (iv) hand moisturisers; (v) tagging near-empty handrub (HR) bottles. Other hospital-wide initiatives included: (vi) Smartphone application for auditing; (vii) 'Speak up for Patient Safety' Campaign in 2017 for staff empowerment; (viii) making HH a key performance indicator.RESULTS: Overall HH compliance increased from a baseline median of 79.6-92.6% in end-2019. Moments 1 and 4 improved from 71 to 92.7% and from 77.6 to 93.2%, respectively. Combined HR and hand wash consumption increased from a baseline median of 82.6 ml/patient day (PD) to 109.2 mL/PD. Health-care-associated rotavirus infections decreased from a baseline median of 4.5 per 10 000 PDs to 1.5 per 10 000 PDs over time.CONCLUSIONS: The 'HH Bundle' of appointing HH champions, active reminders and feedback, patient education and empowerment, availability of hand moisturisers, tagging near-empty hand rub bottles together with hospital-wide initiatives including financial incentives and the 'Speak Up for Patient Safety' campaign successfully improved the overall HH compliance to &gt;90%. These interventions were highly reliable, sustained over 4 years and also reduced health-care-associated rotavirus infection rates.</p

Effect of gibberellic acid and eggshell on Hylocereus polyrhizus

Dragon fruit (Hylocereus polyrhizus) is a tropical fruit. Recently, it has gained interest from the public due to its potential beneficial effects on health. The acclimatization of micropropagated Hylocereus polyrhizus depends on the application of gibberellic acid (GA3 ) to increase plant growth. Eggshells are waste materials from industrial sectors, and they are composed of calcium source that is vital for the development of plant shoots and root. The objective of this research is to investigate the effect of different concentrations of GA3 and eggshell either added individually or in combination on the growth of shoot length and shoot diameter of H. polyrhizus. The result showed the shoot length of the H. polyrhizus increased by approximately 54.69%, from 0.64 ± 0.13 cm to 0.99 ± 0.26 cm, as the concentration of GA3 increased from 0 ppm to 10 ppm. Furthermore, this finding also reported that with eggshells, GA3 showed an adverse effect on the development of shoot diameter. The growth of shoot length and shoot diameter with the addition of eggshell was different, perhaps due to the gibberellic acid affecting the shoot length but not the shoot diameter. Generally, the growth of shoot length and shoot diameter with eggshells was higher in comparison with those without eggshells. With that, we can prove that eggshell is a good additive to promote the growth of H. polyrhizus

Effect of Gibberellic Acid and Eggshell on Hylocereus polyrhizus

Dragon fruit (Hylocereus polyrhizus) is a tropical fruit. Recently, it has gained interest from the public due to its potential beneficial effects on health. The acclimatization of micropropagated Hylocereus polyrhizus depends on the application of gibberellic acid (GA3) to increase plant growth. Eggshells are waste materials from industrial sectors, and they are composed of calcium source that is vital for the development of plant shoots and root. The objective of this research is to investigate the effect of different concentrations of GA3 and eggshell either added individually or in combination on the growth of shoot length and shoot diameter of H. polyrhizus. The result showed the shoot length of the H. polyrhizus increased by approximately 54.69%, from 0.64 ± 0.13 cm to 0.99 ± 0.26 cm, as the concentration of GA3 increased from 0 ppm to 10 ppm. Furthermore, this finding also reported that with eggshells, GA3 showed an adverse effect on the development of shoot diameter. The growth of shoot length and shoot diameter with the addition of eggshell was different, perhaps due to the gibberellic acid affecting the shoot length but not the shoot diameter. Generally, the growth of shoot length and shoot diameter with eggshells was higher in comparison with those without eggshells. With that, we can prove that eggshell is a good additive to promote the growth of H. polyrhizus