Is Spectral Width a Reliable Measure of GRB Emission Physics?

The spectral width and sharpness of unfolded, observed GRB spectra have been presented as a new tool to infer physical properties about GRB emission via spectral fitting of empirical models. Following the tradition of the 'line-of-death', the spectral width has been used to rule out synchrotron emission in a majority of GRBs. This claim is investigated via reexamination of previously reported width measures. Then, a sample of peak-flux GRB spectra are fit with an idealized, physical synchrotron model. It is found that many spectra can be adequately fit by this model even when the width measures would reject it. Thus, the results advocate for fitting a physical model to be the sole tool for testing that model. Finally, a smoothly-broken power law is fit to these spectra allowing for the spectral curvature to vary during the fitting process in order to understand why the previous width measures poorly predict the spectra. It is found that the failing of previous width measures is due to a combination of inferring physical parameters from unfolded spectra as well as the presence of multiple widths in the data beyond what the Band function can model.Comment: Accepted in A&

Link-Prediction Enhanced Consensus Clustering for Complex Networks

Many real networks that are inferred or collected from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that consume the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based algorithm. The framework then merges their multiple outputs into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook

Prospects for Peace and Democracy: Power-Sharing in Sub-Saharan Africa

Sub-Saharan Africa is one of the most politically unstable and undemocratic regions in the world. Theories of power-sharing and recent studies have indicated that institutions that allow for higher levels of power-sharing are often more successful at consolidating democracy and stability in highly divided societies, like those common in Sub-Saharan Africa. By examining the electoral system, executive type, and level of decentralization, this study first determines the level of institutional power-sharing for each of the 48 Sub-Saharan states. Next, it compares these levels of power-sharing to indicators of democracy and state stability to determine if more power-sharing does correspond to greater democracy and stability. Using a bivariate analysis and factoring in region, the data shows that there is a strong and significant correlation between higher levels of institutional power-sharing and higher levels of democracy and state stability in Sub-Saharan Africa

The greening of development theory: good news or bad news for the poor in the Third World?

The present concern for the environment presents an important opportunity with which to pressurise governments and international organisations into making a greater effort to alleviate poverty in the developing world. However, the poor analysis of many environmentalists could actually result in them supporting policies that discriminate against the poor. This paper is concerned with the way in which such poor analysis could reinforce a number of questionable ideas held by others in the development field, such as the belief that a major cause of poverty and environmental destruction in the Third World is an increase in cash cropping. The paper also critically examines the often superficial analysis made by critics of the IMF and World Bank

On the Predictiveness of Single-Field Inflationary Models

We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in principle) for a slightly larger range of Higgs masses. We comment on the origin of the various UV scales that arise at large field values for the SM Higgs, clarifying cut off scale arguments by further developing the formalism of a non-linear realization of $\rm SU_L(2) \times U(1)$ in curved space. We discuss the interesting fact that, outside of Higgs Inflation, the effect of a non-minimal coupling to gravity, even in the SM, results in a non-linear EFT for the Higgs sector. Finally, we briefly comment on post BICEP2 attempts to modify the Higgs Inflation scenario.Comment: 31 pp, 4 figures v4: Minor correction to section 3.1. Main arguments and conclusions unchange