Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding

In this work we consider the problem of estimating a high-dimensional $p \times p$ covariance matrix $\Sigma$, given $n$ observations of confounded data with covariance $\Sigma + \Gamma \Gamma^T$, where $\Gamma$ is an unknown $p \times q$ matrix of latent factor loadings. We propose a simple and scalable estimator based on the projection on to the right singular vectors of the observed data matrix, which we call RSVP. Our theoretical analysis of this method reveals that in contrast to PCA-based approaches, RSVP is able to cope well with settings where the smallest eigenvalue of $\Gamma^T \Gamma$ is close to the largest eigenvalue of $\Sigma$, as well as settings where the eigenvalues of $\Gamma^T \Gamma$ are diverging fast. It is also able to handle data that may have heavy tails and only requires that the data has an elliptical distribution. RSVP does not require knowledge or estimation of the number of latent factors $q$, but only recovers $\Sigma$ up to an unknown positive scale factor. We argue this suffices in many applications, for example if an estimate of the correlation matrix is desired. We also show that by using subsampling, we can further improve the performance of the method. We demonstrate the favourable performance of RSVP through simulation experiments and an analysis of gene expression datasets collated by the GTEX consortium.Supported by an EPSRC First Grant and the Alan Turing Institute under the EPSRC grant EP/N510129/1

Interhemispheric Interactions between the Human Primary Somatosensory Cortices

In the somatosensory domain it is still unclear at which processing stage information reaches the opposite hemispheres. Due to dense transcallosal connections, the secondary somatosensory cortex (S2) has been proposed to be the key candidate for interhemispheric information transfer. However, recent animal studies showed that the primary somatosensory cortex (S1) might as well account for interhemispheric information transfer. Using paired median nerve somatosensory evoked potential recordings in humans we tested the hypothesis that interhemispheric inhibitory interactions in the somatosensory system occur already in an early cortical processing stage such as S1. Conditioning right S1 by electrical median nerve (MN) stimulation of the left MN (CS) resulted in a significant reduction of the N20 response in the target (left) S1 relative to a test stimulus (TS) to the right MN alone when the interstimulus interval between CS and TS was between 20 and 25 ms. No such changes were observed for later cortical components such as the N20/P25, N30, P40 and N60 amplitude. Additionally, the subcortically generated P14 response in left S1 was also not affected. These results document the existence of interhemispheric inhibitory interactions between S1 in human subjects in the critical time interval of 20–25 ms after median nerve stimulation

Donors, Aid and Taxation in Developing Countries: An Overview

Recent years have witnessed rapidly growing donor interest in tax issues in the developing world. This reflects a concern with revenue collection to finance public spending, but also recognition of the centrality of taxation to growth, redistribution and broader state-building and governance goals. Against this backdrop, this paper identifies a series of key issues that demand attention if donors are to improve the quality of their support for tax reform. The focus is not, primarily, on the technical design of tax interventions, but, instead, on seven ‘big picture’ considerations for the design of donor programmes: (a) supporting local leadership of reform efforts; (b) incorporating more systematic political economy analysis into the design and implementation of reform programmes; (c) designing tax reform programmes that seek to foster broader linkages between taxation, state-building and governance; (d) paying careful attention to the complexity of the relationship between aid and tax effort; (e) better designing tax-related conditionality, particularly by developing a more nuanced set of performance indicators; (f) ensuring the effective coordination of donor interventions; and (g) paying greater attention to the international policy context, and particularly the role of tax exemptions for donor projects, tax havens and tax evasion by multinational corporations (MNCs) in undermining developing country tax systems.DfI

Magnetic resonance arthrography of the hip: technique and spectrum of findings in younger patients

Magnetic resonance(MR) imaging is the reference imaging technique in the evaluation of hip abnormalities. However, in some pathological conditions—such as lesions of the labrum, cartilaginous lesions, femoroacetabular impingement, intra-articular foreign bodies, or in the pre-operative work-up of developmental dysplasia of the hip—intra-articular injection of a contrast medium is required to obtain a precise diagnosis. This article reviews the technical aspects, contraindications, normal appearance and potential pitfalls of MR arthrography, and illustrates the radiological appearance of commonly encountered conditions

Foreign aid, instability and governance in Africa

This study contributes to the attendant literature by bundling governance dynamics and focusing on foreign aid instability instead of foreign aid. We assess the role of foreign aid instability on governance dynamics in fifty three African countries for the period 1996-2010. An autoregressive endogeneity-robust Generalized Method of Moments is employed. Instabilities are measured in terms of variance of the errors and standard deviations. Three main aid indicators are used, namely: total aid, aid from multilateral donors and bilateral aid. Principal Component Analysis is used to bundle governance indicators, namely: political governance (voice & accountability and political stability/no violence), economic governance (regulation quality and government effectiveness), institutional governance (rule of law and corruption-control) and general governance (political, economic and institutional governance). Our findings show that foreign aid instability increases governance standards, especially political and general governance. Policy implications are discussed

Graphical model selection for Gaussian conditional random fields in the presence of latent variables

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as “sparsistent” (i.e., capable of recovering the graph structure). We then show how proximal gradient algorithms and semi-definite programming techniques can be employed to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs significantly better than its counterparts, for example, by accommodating more latent variables. Finally, the suggested method is applied to two datasets comprising individual level data on genetic variants and metabolites levels. We show our results replicate better than alternative approaches and show enriched biological signal. Supplementary materials for this article are available online