Crises and Crisis Generations: International Conflict and Military Participation in Politics

Why do states facing high levels of international threat sometimes have militaries that are heavily involved in politics and at other times relatively apolitical, professional militaries? I argue that the answer to this puzzle lies in a state's history of 'acute' international crises rather than its 'chronic' threat environment. Major international crises lead to professionalization and de-politicization of militaries in both the short- and long-term. International crises underscore the need for the military to defend the state and highlight military deficiencies in this regard. Accordingly, major international crises lead to military professionalization and withdrawal from politics in order to increase military effectiveness. This effect persists years, and decades, later due to generational shifts in the officer corps. As the "Crisis Generation" of officers become generals, they bring with them a preference for professionalization and de-politicization. They guide the military towards abstention from politics. I test this theory using a new global dataset on military officers in national governing bodies from 1964-2008 and find strong support for the theory. Major international crises lead to two waves of military withdrawal from government, years apart. Further statistical analysis finds that this effect is most strongly felt in the non-security areas of governing, while in some cases, international crises may lead to militaries increasing their involvement in security policy-making. Further, international crises that end poorly for a state — i.e., defeats or stalemates — are found to drive more rapid waves of military withdrawal from government. The statistical analysis is supported by a case illustration of civil-military relations in the People's Republic of China, which demonstrates that the crisis of the Korean War (1950-53) led to two waves of military professionalization and de-politicization, decades apart. The first occurred immediately after the war. The second wave, occurring in the 1980s, involved wholesale military withdrawal from governing bodies, which was made possible by the ascent of the "Crisis Generation" of officers in the military, who had served as junior officers in the Korean War, decades prior.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of measurements.