Democracy: Concepts, Measures and Relationships

This briefing paper reviews the existing research and debates on the causes and consequences of democracy to provide guidance on the key conceptual and methodological issues surrounding democracy promotion and aid conditionality. It provides three working definitions of democracy; a review of the different strategies and efforts to measure democracy; an examination of the empirical findings on the causes and consequences of democracy; and concludes with a discussion of the dimensions of aid conditionality by examining the efforts by the USAID, the World Bank, and UK Department for International Development (DfID) in linking measures and assessments of governance to the allocation of aid

Human Rights: The Effect of Neighbouring Countries

We examine the geo-political and international spatial aspects of human rights (HR), using a purpose designed data-set. Applying tools from the spatial economics literature, we analyse the impact on a country’s HR performance of geographical proximity to its neighbours. Unlike previous studies, our approach treats this as partly endogenous: one country’s HR performance will affect its neighbours through a variety of potential geographical spillover mechanisms. We start with simple descriptive accounts, using scatter plots, of the geographic history of HR performance. Using a relatively simple spatial weighting model approach we compare each country’s HR performance with what would be predicted by regression on a weighted average of its neighbours’ performance (i.e. weightings depending positively on country population , and negatively upon distance), using a cross sectional and panel dataset of one hundred and sixty countries. We regress measures of population size, distance between countries, the prevalence of war or ethnic conflict, as well as per capita incomes and distribution, to test the general hypothesis that there may be positive spillovers between neighbours’ human rights performance. This is then extended to derive measures of HR performance relative to both economic, social and spatial factors.Human rights, spatial econometrics

Fiber Orientation Estimation Guided by a Deep Network

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

Sr-Isotope Stratigraphy: Assigning Time in the Campanian, Pliensbachian, Toarcian, and Valanginian

The trend of marine 87Sr/86Sr against stratigraphic level through sections, whether linear or not, can identify hiatuses and changing rates of sedimentation through those sections and so be a valuable constraint on attempts to assign numerical ages to sediments on the basis of astrochronology or U/Pb dating of zircons. Here we illustrate that value for the Campanian, Pliensbachian, Toarcian, and Valanginian ages by comparing 87Sr/86Sr profiles for different localities and comparing those to the 87Sr/86Sr profile through time. The analysis reveals possible problems both with current time scales and with some astrochronological calibrations. Our analysis is neither comprehensive nor final; rather, with a few examples, we show how Sr-isotope stratigraphy can be used to moderate other methods of assigning numerical ages to sediments

Coronal and chromospheric physics

Achievements and completed results are discussed for investigations covering solar activity during the solar maximum mission and the solar maximum year; other studies of solar activity and variability; infrared and submillimeter photometry; solar-related atomic physics; coronal and transition region studies; prominence research; chromospheric research in quiet and active regions; solar dynamics; eclipse studies; and polarimetry and magnetic field measurements. Contributions were also made in defining the photometric filterograph instrument for the solar optical telescope, designing the combined filter spectrograph, and in expressing the scientific aims and implementation of the solar corona diagnostic mission

Learning Implicit Brain MRI Manifolds with Deep Learning

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a lowdimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a crosscorrelation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.Comment: SPIE Medical Imaging 201

Energetics, forces, and quantized conductance in jellium modeled metallic nanowires

Energetics and quantized conductance in jellium modeled nanowires are investigated using the local density functional based shell correction method, extending our previous study of uniform in shape wires [C. Yannouleas and U. Landman, J. Phys. Chem. B 101, 5780 (1997)] to wires containing a variable shaped constricted region. The energetics of the wire (sodium) as a function of the length of the volume conserving, adiabatically shaped constriction leads to formation of self selecting magic wire configurations. The variations in the energy result in oscillations in the force required to elongate the wire and are directly correlated with the stepwise variations of the conductance of the nanowire in units of 2e^2/h. The oscillatory patterns in the energetics and forces, and the correlated stepwise variation in the conductance are shown, numerically and through a semiclassical analysis, to be dominated by the quantized spectrum of the transverse states at the narrowmost part of the constriction in the wire.Comment: Latex/Revtex, 11 pages with 5 Postscript figure