Weak convergence rates for stochastic evolution equations and applications to nonlinear stochastic wave, HJMM, stochastic Schr\"odinger and linearized stochastic Korteweg-de Vries equations

We establish weak convergence rates for noise discretizations of a wide class of stochastic evolution equations with non-regularizing semigroups and additive or multiplicative noise. This class covers the nonlinear stochastic wave, HJMM, stochastic Schr\"odinger and linearized stochastic Korteweg-de Vries equation. For several important equations, including the stochastic wave equation, previous methods give only suboptimal rates, whereas our rates are essentially sharp.Comment: 26 pages, minor revisio

Production of metals and compounds by radiation chemistry

Preparation of metals and compounds by radiation induced chemical reactions involves irradiation of metal salt solutions with high energy electrons. This technique offers a method for the preparation of high purity metals with minimum contamination from the container material or the cover gas

Radiation-induced preparation of antimony from solutions of antimony/III/ chloride in organic liquids

Electron irradiation induced separation of Sb from SbCl3 solutions in anhydrous alcohols, ethers, ketones, acids, ethers, and aromatic hydrocarbon

Radiation-induced preparation of metals from their aqueous salt solutions

Metal preparation from aqueous salt solutions by electron irradiatio

Capturing Regular Human Activity through a Learning Context Memory

A learning context memory consisting of two main parts is presented. The first part performs lossy data compression, keeping the amount of stored data at a minimum by combining similar context attributes — the compression rate for the presented GPS data is 150:1 on average. The resulting data is stored in an appropriate data structure highlighting the level of compression. Elements with a high level of compression are used in the second part to form the start and end points of episodes capturing common activity consisting of consecutive events. The context memory is used to investigate how little context data can be stored containing still enough information to capture regular human activity

Unsteady aerodynamic effects in small-amplitude pitch oscillations of an airfoil

High-fidelity wall-resolved large-eddy simulations (LES) are utilized to investigate the flow-physics of small-amplitude pitch oscillations of an airfoil at Re = 100,000. The investigation of the unsteady phenomenon is done in the context of natural laminar flow airfoils, which can display sensitive dependence of the aerodynamic forces on the angle of attack in certain "off-design" conditions. The dynamic range of the pitch oscillations is chosen to be in this sensitive region. Large variations of the transition point on the suction-side of the airfoil are observed throughout the pitch cycle resulting in a dynamically rich flow response. Changes in the stability characteristics of a leading-edge laminar separation bubble has a dominating influence on the boundary layer dynamics and causes an abrupt change in the transition location over the airfoil. The LES procedure is based on a relaxation-term which models the dissipation of the smallest unresolved scales. The validation of the procedure is provided for channel flows and for a stationary wing at Re = 400,000.Comment: 37 pages. 19 figure

Optimal Population Coding, Revisited

Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information