The Helstrom Bound

Quantum state discrimination between two wave functions on a ring is considered. The optimal minimum-error probability is known to be given by the Helstrom bound. A new strategy is introduced by inserting instantaneously two impenetrable barriers dividing the ring into two chambers. In the process, the candidate wave functions, as the insertion points become nodes, get entangled with the barriers and can, if judiciously chosen, be distinguished with smaller error probability. As a consequence, the Helstrom bound under idealised conditions can be violated.Comment: 4 page

Supervisory capital standards: modernise or redesign?

This paper was the distinguished address at the conference "Financial services at the crossroads: capital regulation in the twenty-first century." The conference, held at the Federal Reserve Bank of New York on February 26-27, 1998, was designed to encourage a consensus between the public and private sectors on an agenda for capital regulation in the new century.Bank supervision ; Bank capital

Ukraine, Russia and the EU : Breaking the deadlock in the Minsk process

Although the Minsk process brought about a de-escalation of the conflict in Eastern Ukraine, not all of its 13 points have been implemented, including a ceasefire and withdrawal of heavy weaponry. In the absence of a military option, economic sanctions have become the core instrument of the EU and the US, to respond to Russia’s aggression. At the end of June 2016, when EU Heads of State and Government meet to discuss the extension of sanctions against Russia, they should bear in mind that Russia did not implement the commitments it took upon itself in the framework of the Minsk agreements. Given the persistent deadlock in the Ukraine crisis, the leaders of the EU ought to agree to prolong the sanctions against Russia, push for the renegotiation of the Minsk II agreement and widen the ‘Normandy format’ to include the US and bolster reforms in Ukraine

A ridge-parameter approach to deconvolution

Kernel methods for deconvolution have attractive features, and prevail in the literature. However, they have disadvantages, which include the fact that they are usually suitable only for cases where the error distribution is infinitely supported and its characteristic function does not ever vanish. Even in these settings, optimal convergence rates are achieved by kernel estimators only when the kernel is chosen to adapt to the unknown smoothness of the target distribution. In this paper we suggest alternative ridge methods, not involving kernels in any way. We show that ridge methods (a) do not require the assumption that the error-distribution characteristic function is nonvanishing; (b) adapt themselves remarkably well to the smoothness of the target density, with the result that the degree of smoothness does not need to be directly estimated; and (c) give optimal convergence rates in a broad range of settings.Comment: Published in at http://dx.doi.org/10.1214/009053607000000028 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Mechanics of materials model

The Mechanics of Materials Model (MOMM) is a three-dimensional inelastic structural analysis code for use as an early design stage tool for hot section components. MOMM is a stiffness method finite element code that uses a network of beams to characterize component behavior. The MOMM contains three material models to account for inelastic material behavior. These include the simplified material model, which assumes a bilinear stress-strain response; the state-of-the-art model, which utilizes the classical elastic-plastic-creep strain decomposition; and Walker's viscoplastic model, which accounts for the interaction between creep and plasticity that occurs under cyclic loading conditions