Pakistan’s Nuclear Weapons Program and Implications for US National Security.

This article analyzes Pakistan’s nuclear weapons program and the characteristics of the environment in which the program is nested. These characteristics include Pakistan’s history of internal and external instability; nuclear saber rattling during crises; support for Islamic terrorism in order to advance state goals; indigenous production of many elements of its nuclear forces; possession of delivery and command and control systems with destabilizing characteristics; and finally, nuclear doctrine that appears to advocate first use of nuclear weapons. The article argues that the characteristics of Pakistan’s nuclear weapons program generate threats to US national security interests. The article examines six interrelated and synergistic challenges for US national security: first, Pakistan is engaged in an arms race in Southwest Asia that has negative implications for Pakistan’s stability; second, the threat of nuclear proliferation from Pakistan continues; third, Pakistan’s arsenal characteristics make accidental and/or unauthorized nuclear war more likely; fourth, there is an ongoing possibility of war with India; fifth, Islamist influence is spreading through key sectors of Pakistani society; and finally, there is an increasing danger of state failure in Pakistan

Post-Uruguay Round GATT/WTO Dispute Settlement: Substance, Strengths, Weaknesses, and Causes for Concern

Dispute settlement under the General Agreement on Tariffs and Trade (“GATT”) has come under increasing strain in recent years. The major powers often ignore GATT dispute settlement decisions which do not comport with their economic interests. This situation undermines the credibility of the GATT and threatens the system\u27s framework. If dispute settlement under the GATT continues to be ineffective as it has been through much of the 1980s and early 1990s, GATT member states (“Members”) may well lose faith in the system, begin reimposing the tariffs that were present before the GATT, thereby risking worldwide trade war and possibly consequences as serious as the Great Depression. [FN1] A primary purpose of the Uruguay Round was revision of the GATT dispute settlement system. This paper analyzes the GATT dispute settlement system arising *170 out of the Uruguay Round. The paper initially examines many of the weaknesses existing in the pre-Uruguay Round GATT dispute settlement system, on both theoretical and empirical levels. The paper then discusses the post-Uruguay Round dispute settlement system and how this system attempts to rectify certain weaknesses of the previous system. Finally, the paper discusses problems of the new dispute settlement system, and comments on some implications of these weaknesses. The paper concludes that the Uruguay Round modifications to the GATT dispute settlement system were incomplete and fail to solve certain core problems

Probabilistic models of individual and collective animal behavior

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.Comment: 26 pages, 11 figure

Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus