Intelligence, reason of state and the art of governing risk and opportunity in early modern Europe

Drawing upon primary and secondary historical material, this paper explores the role of intelligence in early modern government. It focuses upon developments in seventeenth- and early-eighteenth-century England, a site-specific genealogical moment in the broader history of state power/knowledges. Addressing a tendency in Foucauldian work to neglect pre-eighteenth-century governance, the analysis reveals a set of interrelated processes which gave rise to an innovative technique for anticipating hazard and opportunity for the state. At the intersection of raison d’État, the evolving art of government, widespread routines of secrecy and a post-Westphalia field of European competition and exchange, intelligence was imagined as a fundamental solution to the concurrent problems of ensuring peace and stability while improving state forces. In the administrative offices of the English Secretary of State, an assemblage of complex and interrelated procedures sought to produce and manipulate information in ways which exposed both possible risks to the state and potential opportunities for expansion and gain. As this suggests, the art of intelligence played an important if largely unacknowledged role in the formation and growth of the early modern state. Ensuring strategic advantage over rivals, intelligence also limited the ability of England's neighbours to dominate trade, control the seas and master the colonies, functioning as a constitutive feature of European balance and equilibrium. As the analysis concludes, understanding intelligence as a form of governmental technique – a way of doing something – reveals an entirely novel way of thinking about and investigating its myriad (historical and contemporary) formations

The cure that may kill Unintended consequences of the INF Treaty

4.50Available from British Library Document Supply Centre- DSC:6217.4525(IEDSS-OP--35) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Geostatistics for Context-Aware Image Classification

Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results. The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective. We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results