CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks

Global Entrepreneurship Monitor United Kingdom: 2007 Executive Report

This monitoring report compares Global Entrepreneurship Monitor (GEM) measures of entrepreneurial activity in the UK with participating G7 countries and the large industrialised or industrialising countries of Brazil, Russia, India and China ("BRIC"). It also summarises entrepreneurial activity within Government Office Regions of the UK

Leveraging Entrepreneurial Ambition and Innovation : A Global Perspective on Entrepreneurship, Competitiveness and Development

The study described in this report combines two unique datasets, the World Economic Forum’s Global Competitiveness Index data, which ranks the economic competitiveness of 144 economies, and Global Entrepreneurship Monitor’s assessment of entrepreneurial activity across 70 economies. Using five years of data from both sets, the study analyses a sample of 44 economies by first examining three aspects of entrepreneurial activity, then grouping economies into five types of entrepreneurial clusters, and finally developing a deeper understanding of each type of cluster. Lastly, the study delves into what policymaking best benefits the unique characteristics of different economies

GluN2A NMDA Receptor Enhancement Improves Brain Oscillations, Synchrony, and Cognitive Functions in Dravet Syndrome and Alzheimer's Disease Models.

NMDA receptors (NMDARs) play subunit-specific roles in synaptic function and are implicated in neuropsychiatric and neurodegenerative disorders. However, the in vivo consequences and therapeutic potential of pharmacologically enhancing NMDAR function via allosteric modulation are largely unknown. We examine the in vivo effects of GNE-0723, a positive allosteric modulator of GluN2A-subunit-containing NMDARs, on brain network and cognitive functions in mouse models of Dravet syndrome (DS) and Alzheimer's disease (AD). GNE-0723 use dependently potentiates synaptic NMDA receptor currents and reduces brain oscillation power with a predominant effect on low-frequency (12-20 Hz) oscillations. Interestingly, DS and AD mouse models display aberrant low-frequency oscillatory power that is tightly correlated with network hypersynchrony. GNE-0723 treatment reduces aberrant low-frequency oscillations and epileptiform discharges and improves cognitive functions in DS and AD mouse models. GluN2A-subunit-containing NMDAR enhancers may have therapeutic benefits in brain disorders with network hypersynchrony and cognitive impairments