Taming the Evil: US Non-proliferation Coercive Diplomacy and the Counter-strategies of Iran and North Korea after the Cold War

In the 40 years since the end of World War II, the most critical strategic problem for the US was containment of the Soviet Union. During the Cold War, scholars and experts focused on building deterrence theories on how to confront communist aggression. In comparison, the theory of diplomatic coercion, which tries to use threats or a limited amount of force to influence the behaviour of another by making it choose to comply, was popular neither among decision-makers nor scholars. Since a favourable international environment for applying coercive diplomacy began after the Cold War finished in the 1990s, coercive diplomacy and the coercion literature have proved to be less rich and less cumulative than that of other political theories. However, regardless of this weak enthusiasm for it, the concept of coercion was adopted as state foreign policy and diplomatic coercion was executed as a strategy. The US administrations after the fall of the Soviet Union have implemented coercive diplomacy to influence their adversaries. The non-proliferation policy of the US was no exception. Regardless of the differences in the doctrines and policies of each administration, Clinton, Bush and Obama had a consistent policy on nuclear non-proliferation. Having become the hegemonic state of unipolar system with the ability to conduct a war in any place in the world, the execution of coercion was the most convenient policy strategy for the US among the other alternatives. From a basis of dominant military strength and economic power, the Clinton, Bush and Obama administrations attempted to dismantle the nuclear programmes of Iran and North Korea by every conceivable means, utilizing hard power, soft power and smart power. The coercive, non-coercive and persuasive inducements of coercive diplomacy were applied to stop these nuclear programmes. None of the administrations allowed the full fledge nuclear programmes of Iran and North Korea. Instead, they labelled Iran and North Korea rogue states or ‘axis of evil’ during the span of the three presidents. Admittedly, the Obama administration showed differences in terms of rhetoric, but the ‘strategic patience’ which it applied to Iran and North Korea during its first term was not much different from the policy of its predecessors. Moreover, Obama applied the most severe economic sanctions, which even prohibited the Iranian oil trade. However, the coercive diplomacy of the US administrations did not have tangible success in disarming these states of their nuclear programmes; instead, they increased their nuclear capabilities. Although a nuclear deal has recently been reached in the Iranian case, it will take a process lasting 15 years to complete the settlement. It seems that US coercive diplomacy is most likely to be maintained during this period. This study focuses on the non-proliferation coercive diplomacy of the US against the ‘axis of evil’ of Iran and North Korea and their counterstrategies in order to examine the dispute process as a whole and to provide more efficient policy proposals regarding the subject

Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test

Maximum mean discrepancy (MMD) refers to a general class of nonparametric two-sample tests that are based on maximizing the mean difference over samples from one distribution $P$ versus another $Q$, over all choices of data transformations $f$ living in some function space $\mathcal{F}$. Inspired by recent work that connects what are known as functions of $\textit{Radon bounded variation}$ (RBV) and neural networks (Parhi and Nowak, 2021, 2023), we study the MMD defined by taking $\mathcal{F}$ to be the unit ball in the RBV space of a given smoothness order $k \geq 0$. This test, which we refer to as the $\textit{Radon-Kolmogorov-Smirnov}$ (RKS) test, can be viewed as a generalization of the well-known and classical Kolmogorov-Smirnov (KS) test to multiple dimensions and higher orders of smoothness. It is also intimately connected to neural networks: we prove that the witness in the RKS test -- the function $f$ achieving the maximum mean difference -- is always a ridge spline of degree $k$, i.e., a single neuron in a neural network. This allows us to leverage the power of modern deep learning toolkits to (approximately) optimize the criterion that underlies the RKS test. We prove that the RKS test has asymptotically full power at distinguishing any distinct pair $P \not= Q$ of distributions, derive its asymptotic null distribution, and carry out extensive experiments to elucidate the strengths and weakenesses of the RKS test versus the more traditional kernel MMD test

Semi-parametric contextual bandits with graph-Laplacian regularization

Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-stationarity could harm performances. Another prevalent human behavior is social interaction which has become available in a form of a social network or graph structure. As a result, graph-based contextual bandits have received much attention. In this paper, we propose SemiGraphTS, a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric reward model. Our algorithm is the first to be proposed in this setting. We derive an upper bound of the cumulative regret that can be expressed as a multiple of a factor depending on the graph structure and the order for the semi-parametric model without a graph. We evaluate the proposed and existing algorithms via simulation and real data example

DGKι regulates presynaptic release during mGluR-dependent LTD

Diacylglycerol (DAG) is a second messenger acting in synaptic signalling. The DAG metabolizing enzyme DGKι regulates presynaptic neurotransmitter release and PSD-95 family proteins promote its synaptic localization

DGKi regulates presynaptic release during mGluR-dependent LTD

Diacylglycerol (DAG) is an important lipid second messenger. DAG signalling is terminated by conversion of DAG to phosphatidic acid (PA) by diacylglycerol kinases (DGKs). The neuronal synapse is a major site of DAG production and action; however, how DGKs are targeted to subcellular sites of DAG generation is largely unknown. We report here that postsynaptic density (PSD)-95 family proteins interact with and promote synaptic localization of DGKl. In addition, we establish that DGKl acts presynaptically, a function that contrasts with the known postsynaptic function of DGKl, a close relative of DGKl. Deficiency of DGKl in mice does not affect dendritic spines, but leads to a small increase in presynaptic release probability. In addition, DGKl-/- synapses show a reduction in metabotropic glutamate receptor-dependent long-term depression (mGluR-LTD) at neonatal (∼2 weeks) stages that involve suppression of a decrease in presynaptic release probability. Inhibition of protein kinase C normalizes presynaptic release probability and mGluR-LTD at DGKl-/- synapses. These results suggest that DGKl requires PSD-95 family proteins for synaptic localization and regulates presynaptic DAG signalling and neurotransmitter release during mGluR-LTD.This work was supported by the NIH R01-CA95463 grant (to MKT), the Neuroscience Program (to S-YC; 2009-0081468), and the National Creative Research Initiative Program of the Korean Ministry of Education, Science, and Technology (to EK)