Trade related business climate and manufacturing export performance in Africa: A firm-level analysis

Africa continues to be marginalised in world trade of manufactured goods, despite reductions in tariffs and non-tariff barriers. This paper investigates whether high business and trade costs associated with Africa’s trade-related infrastructure, trade institutions and the regulatory environment have contributed towards its mediocre trade performance. The paper focuses on eight African countries - Egypt, Kenya, Madagascar, Mauritius, Morocco, South Africa, Tanzania and Zambia - using the World Bank’s investment climate surveys. The results of the study suggest that the business climate, as measured using principal components for micro-level supply constraints, macroeconomic conditions and the legal environment, is closely associated with firm-level export propensity. Improvements in domestic policy may therefore have a considerable positive impact on manufacturing export performance in Africa

Crossed Aphasia in a Patient with Anaplastic Astrocytoma of the Non-Dominant Hemisphere

Aphasia describes a spectrum of speech impairments due to damage in the language centers of the brain. Insult to the inferior frontal gyrus of the dominant cerebral hemisphere results in Broca\u27s aphasia - the inability to produce fluent speech. The left cerebral hemisphere has historically been considered the dominant side, a characteristic long presumed to be related to a person\u27s handedness . However, recent studies utilizing fMRI have shown that right hemispheric dominance occurs more frequently than previously proposed and despite a person\u27s handedness. Here we present a case of a right-handed patient with Broca\u27s aphasia caused by a right-sided brain tumor. This is significant not only because the occurrence of aphasia in right-handed-individuals with right hemispheric brain damage (so-called crossed aphasia ) is unusual but also because such findings support dissociation between hemispheric linguistic dominance and handedness. © 2017, EduRad. All rights reserved

Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and because all the computations are parallelizable, the parallel form of the algorithm is sublinear in the number of data points. We provide example algorithms to implement the sparse matrix routines and experimentally test the method using both simulated and real data.Comment: Presented at Machine Learning in Signal Processing (MLSP2017

Deep Gaussian Processes

In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.Comment: 9 pages, 8 figures. Appearing in Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 201

Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown environmental influences. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. 

Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an
eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, PANAMA can more accurately distinguish between true genetic association signals and confounding variation. 

We applied our model and compared it to existing methods on a variety of datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, PANAMA not only identified a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies