What Drives Fiscal Decentralisation?

This paper investigates the determinants of fiscal decentralisation, focusing in particular on the impact of the level of income on the level of fiscal decentralisation. Various measures of fiscal decentralisation, several of them novel in this context, are employed in a cross-country econometric model to test established and more recent hypotheses. Paying careful attention to variable measurement, model specification and sample coverage, the results suggest that there are significant relationships between a range of factors, including income, geographical size, population density, population diversity, military expenditure, the structure of the public sector and openness to trade, and fiscal decentralisation. However, these relationships may be more complicated than previously reported. For the entire sample and for the OECD subsample a positive relationship between income and decentralisation is found, which corroborates the results found in earlier studies. However, for the middle and lower income nations, higher income is found to be associated with less decentralisation.

A Binary Neural Network Framework for Attribute Selection and Prediction

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus journey times from traffic sensor data and show how attribute selection can both speed our k-NN and increase the prediction accuracy by removing noise and redundant attributes from the data

Compatible or Conflicting: The Promotion of a High Level of Employment and the Consumer Welfare Standard Under Article 101

The antitrust, or competition, regime of the European Union (EU) differs substantially from that of the United States, because EU competition law forms part of the EU Treaties and is therefore imbibed with the multiple values of the European Union itself. Accordingly, it is by no means clear or settled if the anti-cartel law of the European Union, Article 101 TFEU, must focus solely on a consumer welfare standard or must also consider the broad and multiple policy aims enshrined in the EU Treaties. If Article 101 must balance multiple aims, this would be in stark contrast to Section 1 of the Sherman Act, where the sole goal of consumer welfare has long been established. This Article will seek to demonstrate that when an agreement is examined under Article 101, any anti-competitive impact that is detrimental to consumer welfare must be balanced against the positive effect on the policy goals of the EU (with the Article focusing particularly on employment issues). The Article further proposes that a “bifurcated balancing approach” should be adopted, with economic efficiency concerns being examined under Article 101(1) and broader policy goals being considered in Article 101(3). The proposals made in this Article are not wholly without controversy, but are supported by the case law of the European Court of Justice

A HADOOP-Based Framework for Parallel and Distributed Feature Selection

In this paper, we introduce a theoretical basis for a Hadoop-based framework for parallel and distributed feature selection. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of four feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop MapReduce. Hadoop allows parallel and distributed processing so each feature selector can be processed in parallel and multiple feature selectors can be processed together in parallel allowing multiple feature selectors to be compared. We identify commonalities among the four features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all four feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop

Optimising Activation of Bus Pre-signals

This report describes preliminary analysis of strategies to activate and deactivate a bus pre-signal using vehicle count data. The bus pre-signal currently operates during preset times to regulate access to a length of road controlled at the other end by vehicle-actuated traffic signals. However, vehicle flows at the pre-signal vary on a daily basis so a more demand-based approach would be more effective. There has been much research performed to optimise pre-signal cycle times and bus priority at pre-signals. We focus on identifying the optimal strategy to activate and deactivate the bus pre-signal using vehicle demand rather than the current fixed time strategy. The ideal strategy should be stable, robust, consistent and timely. We investigate strategies using vehicle counts, queueing theory and estimation and prediction. Our recommended strategy combines aspects of all three areas

DYRK1a inhibitor mediated rescue of Drosophila models of Alzheimer’s disease-Down Syndrome phenotypes

Alzheimer’s disease (AD) is the most common neurodegenerative disease which is becoming increasingly prevalent due to ageing populations resulting in huge social, economic, and health costs to the community. Despite the pathological processing of genes such as Amyloid Precursor Protein (APP) into Amyloid-β and Microtubule Associated Protein Tau (MAPT) gene, into hyperphosphorylated Tau tangles being known for decades, there remains no treatments to halt disease progression. One population with increased risk of AD are people with Down syndrome (DS), who have a 90% lifetime incidence of AD, due to trisomy of human chromosome 21 (HSA21) resulting in three copies of APP and other AD-associated genes, such as DYRK1A (Dual specificity tyrosine-phosphorylation-regulated kinase 1A) overexpression. This suggests that blocking DYRK1A might have therapeutic potential. However, it is still not clear to what extent DYRK1A overexpression by itself leads to AD-like phenotypes and how these compare to Tau and Amyloid-β mediated pathology. Likewise, it is still not known how effective a DYRK1A antagonist may be at preventing or improving any Tau, Amyloid-β and DYRK1a mediated phenotype. To address these outstanding questions, we characterised Drosophila models with targeted overexpression of human Tau, human Amyloid-β or the fly orthologue of DYRK1A, called minibrain (mnb). We found targeted overexpression of these AD-associated genes caused degeneration of photoreceptor neurons, shortened lifespan, as well as causing loss of locomotor performance, sleep, and memory. Treatment with the experimental DYRK1A inhibitor PST-001 decreased pathological phosphorylation of human Tau [at serine (S) 262]. PST-001 reduced degeneration caused by human Tau, Amyloid-β or mnb lengthening lifespan as well as improving locomotion, sleep and memory loss caused by expression of these AD and DS genes. This demonstrated PST-001 effectiveness as a potential new therapeutic targeting AD and DS pathology

AURA-Alert: The use of Binary Associative Memories for Condition Monitoring Applications

Many Condition Monitoring (CM) domains are suffering from the dual challenges of substantial increases in the volumes of data being produced and collected by sensing systems, and the challenges of modelling increasing complexity in the remote monitored systems. These two issues give rise to the problem that fast and reliable data mining of CM data is a computationally demanding task for real-time (or near real-time) applications. We present the use of AURA [1], a class of binary associative network built on correlation matrix memories (CMMs), as an underpinning technology for efficient, scalable pattern recognition in complex and large scale CM applications. AURA is a class of binary neural network. However, it has a number of advantages over standard neural network techniques for CM pattern classification tasks. These include; high levels of data compression, one-pass training for on-line training, a scalable architecture that can be readily mapped onto high performance computing platforms, and a sound theoretical basis to determine the bounds of the system operation. We describe applications illustrating how the AURA system can be optimised to create an extremely efficient and scalable k-nearest neighbour classifier for multi-variate models. We will also illustrate how the one-pass training capability of the AURA system can be used as the basis of normality and exception modelling in complex CM systems. This latter application has particularly powerful advantages for fault detection models in domains which are characterised by highly dynamic trends or drifting in the standard operational mode of a system, and which, as a result, are extremely difficult to accurately model. The application of the AURA techniques will be illustrated with industry led exemplars in the transport and energy sectors