Book review: why is aid not effective in the Palestinian case and how this can be changed?

Despite for many years receiving one of the highest per capita aid worldwide, the economies of the West Bank and Gaza Strip have failed to achieve any lasting developmental outcomes and suffer from major weaknesses which undermine their very survival. This book argues that the dominant, mainstream approach to the study of aid and aid effectiveness is theoretically and empirically inadequate for a comprehensive understanding and analysis of the workings of aid in developing countries. Alaa Tartir finds that this book adds an important, distinctive and timely contribution to the scholarly work on Palestine. The Political Economy of Aid in Palestine: Relief from Conflict or Development Delayed? Sahar Taghdisi-Rad. Routledge. 2011

To Parallelize or Not to Parallelize, Speed Up Issue

Running parallel applications requires special and expensive processing resources to obtain the required results within a reasonable time. Before parallelizing serial applications, some analysis is recommended to be carried out to decide whether it will benefit from parallelization or not. In this paper we discuss the issue of speed up gained from parallelization using Message Passing Interface (MPI) to compromise between the overhead of parallelization cost and the gained parallel speed up. We also propose an experimental method to predict the speed up of MPI applications

Parallel Performance of MPI Sorting Algorithms on Dual-Core Processor Windows-Based Systems

Message Passing Interface (MPI) is widely used to implement parallel programs. Although Windowsbased architectures provide the facilities of parallel execution and multi-threading, little attention has been focused on using MPI on these platforms. In this paper we use the dual core Window-based platform to study the effect of parallel processes number and also the number of cores on the performance of three MPI parallel implementations for some sorting algorithms

X-ray Spectroscopy of Bursts from SGR 1806-20 with RXTE

We report on new RXTE X-ray spectral analysis of bursts from SGR 1806-20, the most prolific SGR source known. Previous studies of bursts from this source revealed a remarkable lack of spectral variability both in single bursts as well as from burst to burst. We present here some of the first evidence for significant spectral evolution within SGR bursts. We find that optically thin thermal bremsstrahlung (OTTB) spectra including photoelectric absorption provide the best fits to most bursts, however, other models (power law, Band GRB) can also produce statistically acceptable fits. We confirm the existence of a rolloff in the photon number spectrum below 5 keV.Comment: 5 pages, 5 figures, LaTeX AIP Proceedings article, to appear in the proceedings of the 4th BATSE Gamma-Ray burst workshop in Hunstville, A

Spectral density of the non-backtracking operator

The non-backtracking operator was recently shown to provide a significant improvement when used for spectral clustering of sparse networks. In this paper we analyze its spectral density on large random sparse graphs using a mapping to the correlation functions of a certain interacting quantum disordered system on the graph. On sparse, tree-like graphs, this can be solved efficiently by the cavity method and a belief propagation algorithm. We show that there exists a paramagnetic phase, leading to zero spectral density, that is stable outside a circle of radius $\sqrt{\rho}$, where $\rho$ is the leading eigenvalue of the non-backtracking operator. We observe a second-order phase transition at the edge of this circle, between a zero and a non-zero spectral density. That fact that this phase transition is absent in the spectral density of other matrices commonly used for spectral clustering provides a physical justification of the performances of the non-backtracking operator in spectral clustering.Comment: 6 pages, 6 figures, submitted to EP

Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation

The completion of low rank matrices from few entries is a task with many practical applications. We consider here two aspects of this problem: detectability, i.e. the ability to estimate the rank $r$ reliably from the fewest possible random entries, and performance in achieving small reconstruction error. We propose a spectral algorithm for these two tasks called MaCBetH (for Matrix Completion with the Bethe Hessian). The rank is estimated as the number of negative eigenvalues of the Bethe Hessian matrix, and the corresponding eigenvectors are used as initial condition for the minimization of the discrepancy between the estimated matrix and the revealed entries. We analyze the performance in a random matrix setting using results from the statistical mechanics of the Hopfield neural network, and show in particular that MaCBetH efficiently detects the rank $r$ of a large $n\times m$ matrix from $C(r)r\sqrt{nm}$ entries, where $C(r)$ is a constant close to $1$. We also evaluate the corresponding root-mean-square error empirically and show that MaCBetH compares favorably to other existing approaches.Comment: NIPS Conference 201