4,662 research outputs found

    The Central Intelligence Agency’s armed Remotely Piloted Vehicle-supported counter-insurgency campaign in Pakistan – a mission undermined by unintended consequences?

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    This paper views America's 'drones-first' counter-insurgency effort in Pakistan through the lens of Merton's theory of the unintended consequences of purposive action. It also references Beck’s Risk Society thesis, America’s Revolution in Military Affairs doctrine, Toft’s theory of isomorphic learning, Langer’s theory of mindfulness, Highly Reliable Organisations theory and the social construction of technology (SCOT) argument. With reference to Merton’s theory, the CIA-directed armed Remotely Piloted Vehicle (RPV) campaign has manifest functions, latent functions and latent dysfunctions. Measured against numbers of suspected insurgents killed, the campaign can be judged a success. Measured against the level of collateral damage or the state of US-Pakistan relations, the campaign can be judged a failure. Values determine the choice of metrics. Because RPV operations eliminate risk to American service personnel, and because this is popular with both US citizens and politicians, collateral damage (the killing of civilians) is not considered a policy-changing dysfunction. However, the latent dysfunctions of America's drones-first policy may be so great as to undermine that policy's intended manifest function – to make a net contribution to the War on Terror. In Vietnam the latent dysfunctions of Westmoreland’s attritional war undermined America’s policy of containment. Vietnam holds a lesson for the Obama administration.Publisher PD

    Deformable Model Retrieval Based on Topological and Geometric Signatures

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    With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created. To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, i.e., models of different poses, and these methods are generally very slow. In this paper, we present a novel method for efficient matching and retrieval of 3D deformable models. Our research idea stresses on using both topological and geometric features at the same time. First, we propose Topological Point Ring (TPR) analysis to locate reliable topological points and rings. Second, we capture both local and global geometric information to characterize each of these topological features. To compare the similarity of two models, we adapt the Earth Mover Distance (EMD) as the distance function, and construct an indexing tree to accelerate the retrieval process. We demonstrate the performance of the new method, both in terms of accuracy and speed, through a large number of experiments
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