Does Foreign Aid Help Reduce Income Inequality?

The aim of this paper is to provide empirical evidence on the link between foreign aid and income inequality for the period 1973-2002. Since simple cross-country regressions cannot be taken as `true` time series findings, we also focus on dynamic panel data techniques, which allow accounting for potential simultaneity and heterogeneity problems. We do not find very robust evidence that foreign aid is conducive to the improvement of the distribution of income, even when the quality of institutions is taken into account. This finding is consistent with recent empirical research on aid and growth that shows that such a link is weak at best.

Classification of high-dimensional data based on multiple testing methods

Supervised and unsupervised classification are common topics in machine learning in both scientific and industrial fields, which usually involve three tasks: prediction, exploration, and explanation. False discovery rate (FDR) theory has a close connection to classical classification theory, which must be employed in a sophisticated way to achieve good performance in various contexts. The study aims to explore novel supervised classifiers and unsupervised classification approaches for functional data and high-dimensional data in genome study by using FDR, respectively. One work develops a novel classifier for functional data by casting the classification problem into a multiple testing task, which involves using statistical depth functions. The other two works essentially deal with p-values or tail-areas by using FDR in the large scale testing problem. One work proposes a novel algorithm to yield reproducible differential expression analysis for microarray and RNA-Seq data. The proposed algorithm combines the cross-validation type subsampling and false discovery rate, where the p-values obtained from the training data are used to fit a mixture of baseline and signal distributions by using the EM algorithm, which is in turn used to screen the significance for the p-values obtained from the testing data. Another work proposes a novel weighted p-value approach to explore the association between microRNAs and COPD emphysema severity by regulating the mRNA expressions, while integrating patient phenotype information. This proposed method can be applied to study the causality between miRNA and any particular disease, by exploring the precise role of miRNA in regulating genes

A Large-field J=1-0 Survey of CO and Its Isotopologues Toward the Cassiopeia A Supernova Remnant

We have conducted a large-field simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ emission toward the Cassiopeia A (Cas A) supernova remnant (SNR), which covers a sky area of $3.5^{\circ}\times3.1^{\circ}$. The Cas giant molecular cloud (GMC) mainly consists of three individual clouds with masses on the order of $10^4-10^5\ M_{\odot}$. The total mass derived from the $\rm{^{13}CO}$ emission of the GMC is 2.1$\times10^{5}\ M_{\odot}$ and is 9.5$\times10^5\ M_{\odot}$ from the $\rm{^{12}CO}$ emission. Two regions with broadened (6$-$7 km s$^{-1}$) or asymmetric $^{12}$CO line profiles are found in the vicinity (within a 10$'\times10'$ region) of the Cas A SNR, indicating possible interactions between the SNR and the GMC. Using the GAUSSCLUMPS algorithm, 547 $^{13}$CO clumps are identified in the GMC, 54$\%$ of which are supercritical (i.e. $\alpha_{\rm{vir}}<2$). The mass spectrum of the molecular clumps follows a power-law distribution with an exponent of $-2.20$. The pixel-by-pixel column density of the GMC can be fitted with a log-normal probability distribution function (N-PDF). The median column density of molecular hydrogen in the GMC is $1.6\times10^{21}$ cm$^{-2}$ and half the mass of the GMC is contained in regions with H$_2$ column density lower than $3\times10^{21}$ cm$^{-2}$, which is well below the threshold of star formation. The distribution of the YSO candidates in the region shows no agglomeration.Comment: 24 pages, 18 figure