The development and application of structural priors for diffuse optical imaging in infants from newborn to two years of age

This thesis describes the development and application of age-appropriate structural priors to improve the localisation accuracy of diffuse optical tomography (DOT) approaches in infants aged from birth to two years of age. Knowledge of the target cranial anatomy, known as a structural prior, is required to produce three-dimensional images localising concentration changes to the cortex. A structural prior would ideally be subject-specific, i.e. derived from structural magnetic resonance imaging (MRI) data from each specific subject. Requiring a structural scan from every infant participant, however, is not feasible and undermines many of the benefits of DOT. A review was conducted to catalogue available infant structural MRI data, and selected data was then used to produce structural priors for infants aged 1- to 24-months. Conventional analyses using functional near-infrared spectroscopy (fNIRS) implicitly assume that head size and array position are constant across infants. Using DOT, the validity of assuming these parameters constant in a longitudinal infant cohort was investigated. The results show that this assumption is reasonable at the group-level in infants aged 5- to 12-months but becomes less valid for smaller group sizes. A DOT approach was determined to illicit more subtle effects of activation, particularly for smaller group sizes and expected responses. Using state-of-the-art MRI data from the Developing Human Connectome Project, a database of structural priors of the neonatal head was produced for infants aged pre-term to term-equivalent age. A leave-one-out approach was used to determine how best to find a match between a given infant and a model from the database, and how best to spatially register the model to minimise the anatomical and localisation errors relative to subject-specific anatomy. Model selection based on the 10/20 scalp positions was determined to be the best method (of those based on external features of the head) to minimise these errors

Exploiting Shared Representations for Personalized Federated Learning

Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. However, the fruits of representation learning have yet to be fully-realized in federated settings. Although data in federated settings is often non-i.i.d. across clients, the success of centralized deep learning suggests that data often shares a global feature representation, while the statistical heterogeneity across clients or tasks is concentrated in the labels. Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. We prove that this method obtains linear convergence to the ground-truth representation with near-optimal sample complexity in a linear setting, demonstrating that it can efficiently reduce the problem dimension for each client. Further, we provide extensive experimental results demonstrating the improvement of our method over alternative personalized federated learning approaches in heterogeneous settings

Biokinetics Of microbial consortia using biogenic sulfur as a novel electron donor for sustainable denitrification

In this study, the biokinetics of autotrophic denitrification with biogenic S0 (ADBIOS) for the treatment of nitrogen pollution in wastewaters were investigated. The used biogenic S0, a by-product of gas desulfurization, was an elemental microcrystalline orthorhombic sulfur with a median size of 4.69 µm and a specific surface area of 3.38 m2/g, which made S0 particularly reactive and bioavailable. During denitritation, the biomass enriched on nitrite (NO2–) was capable of degrading up to 240 mg/l NO2–-N with a denitritation activity of 339.5 mg NO2–-N/g VSS·d. The use of biogenic S0 induced a low NO2–-N accumulation, hindering the NO2–-N negative impact on the denitrifying consortia and resulting in a specific denitrification activity of 223.0 mg NO3–-N/g VSS·d. Besides Thiobacillus being the most abundant genus, Moheibacter and Thermomonas were predominantly selected for denitrification and denitritation, respectively

The venture crowd: crowdfunding equity investments into business

Crowdfunding is big business. The idea of financing projects or businesses with small contributions from large numbers of people is catching on in a big way and now accounts for significant amounts of money. In 2011 alone, $1.5 billion was raised through crowdfunding for projects and businesses in need of funds. Not only does the model provide finance but also access to a large number of people who can test and market an idea. Crowdfunding takes a number of different forms, the most successful of which has been the reward–based model where participants receive non–financial rewards in exchange for donating to a project. The model effectively harnesses not only the contributors’ desire for the reward but also the intrinsic or social motivations to back a project. Other forms of the model are, however, also growing rapidly. The most recent of these is equity crowdfunding, where individuals receive small stakes in a privately owned young business in return for investment

Banking on each other: peer-to-peer lending to business: evidence from funding circle

As banks retrench in the wake of the financial crisis, small businesses have found it increasingly hard to access the finance they need to grow. But there is some cause for optimism. New providers of business finance are stepping into the space left by banks, and are devising innovative business models, often taking advantage of new technologies and different sources of capital. One such model that has grown rapidly in recent years is peer–to–peer financing. This report seeks to cast some light on the emerging field of peer–to–peer lending to businesses, using a large set of data collected through Funding Circle, the largest peer–to–peer business lending site in the UK. Funding Circle has facilitated approximately £100 million in loans to over 1,700 companies to date (as of April 2013). This report looks at the characteristics of both Funding Circle’s borrowers and lenders, which enables the examination of the decision to seek or lend money through the peer–to–peer sites or ‘platforms’. It is the first attempt to analyse the peer–to–peer lending to businesses model using proprietary data from from 630 investors and 89 companies

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks

Feature learning, i.e. extracting meaningful representations of data, is quintessential to the practical success of neural networks trained with gradient descent, yet it is notoriously difficult to explain how and why it occurs. Recent theoretical studies have shown that shallow neural networks optimized on a single task with gradient-based methods can learn meaningful features, extending our understanding beyond the neural tangent kernel or random feature regime in which negligible feature learning occurs. But in practice, neural networks are increasingly often trained on {\em many} tasks simultaneously with differing loss functions, and these prior analyses do not generalize to such settings. In the multi-task learning setting, a variety of studies have shown effective feature learning by simple linear models. However, multi-task learning via {\em nonlinear} models, arguably the most common learning paradigm in practice, remains largely mysterious. In this work, we present the first results proving feature learning occurs in a multi-task setting with a nonlinear model. We show that when the tasks are binary classification problems with labels depending on only $r$ directions within the ambient $d\gg r$-dimensional input space, executing a simple gradient-based multitask learning algorithm on a two-layer ReLU neural network learns the ground-truth $r$ directions. In particular, any downstream task on the $r$ ground-truth coordinates can be solved by learning a linear classifier with sample and neuron complexity independent of the ambient dimension $d$, while a random feature model requires exponential complexity in $d$ for such a guarantee