Patterned Irradiation of YBa_2Cu_3O_(7-x) Thin Films

We present a new experiment on YBa_2Cu_3O_{7-x} (YBCO) thin films using spatially resolved heavy ion irradiation. Structures consisting of a periodic array of strong and weak pinning channels were created with the help of metal masks. The channels formed an angle of +/-45 Deg with respect to the symmetry axis of the photolithographically patterned structures. Investigations of the anisotropic transport properties of these structures were performed. We found striking resemblance to guided vortex motion as it was observed in YBCO single crystals containing an array of unidirected twin boundaries. The use of two additional test bridges allowed to determine in parallel the resistivities of the irradiated and unirradiated parts as well as the respective current-voltage characteristics. These measurements provided the input parameters for a numerical simulation of the potential distribution of the Hall patterning. In contrast to the unidirected twin boundaries in our experiment both strong and weak pinning regions are spatially extended. The interfaces between unirradiated and irradiated regions therefore form a Bose-glass contact. The experimentally observed magnetic field dependence of the transverse voltage vanishes faster than expected from the numerical simulation and we interpret this as a hydrodynamical interaction between a Bose-glass phase and a vortex liquid.Comment: 7 pages, 8 Eps figures included. Submitted to PR

On the construction of probabilistic Newton-type algorithms

It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions. Importantly, this understanding allows us to safely start assembling probabilistic Newton-type algorithms, applicable in situations where we only have access to noisy observations of the cost function and its derivatives. This is where our interest lies. We make contributions to the use of the non-parametric and probabilistic Gaussian process models in solving these stochastic optimisation problems. Specifically, we present a new algorithm that unites these approximations together with recent probabilistic line search routines to deliver a probabilistic quasi-Newton approach. We also show that the probabilistic optimisation algorithms deliver promising results on challenging nonlinear system identification problems where the very nature of the problem is such that we can only access the cost function and its derivative via noisy observations, since there are no closed-form expressions available

A Bayesian Filtering Algorithm for Gaussian Mixture Models

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper

Colour reverse learning and animal personalities: the advantage of behavioural diversity assessed with agent-based simulations

Foraging bees use colour cues to help identify rewarding from unrewarding flowers, but as conditions change, bees may require behavioural flexibility to reverse their learnt preferences. Perceptually similar colours are learnt slowly by honeybees and thus potentially pose a difficult task to reverse-learn. Free-flying honeybees (N = 32) were trained to learn a fine colour discrimination task that could be resolved at ca. 70% accuracy following extended differential conditioning, and were then tested for their ability to reverse-learn this visual problem multiple times. Subsequent analyses identified three different strategies: ‘Deliberative-decisive’ bees that could, after several flower visits, decisively make a large change to learnt preferences; ‘Fickle- circumspect’ bees that changed their preferences by a small amount every time they encountered evidence in their environment; and ‘Stay’ bees that did not change from their initially learnt preference. The next aim was to determine if there was any advantage to a colony in maintaining bees with a variety of decision-making strategies. To understand the potential benefits of the observed behavioural diversity agent-based computer simulations were conducted by systematically varying parameters for flower reward switch oscillation frequency, flower handling time, and fraction of defective ‘target’ stimuli. These simulations revealed that when there is a relatively high frequency of reward reversals, fickle-circumspect bees are more efficient at nectar collection. However, as the reward reversal frequency decreases the performance of deliberative-decisive bees becomes most efficient. These findings show there to be an evolutionary benefit for honeybee colonies with individuals exhibiting these different strategies for managing resource change. The strategies have similarities to some complex decision-making processes observed in humans, and algorithms implemented in artificial intelligence systems

Binary morphological shape-based interpolation applied to 3-D tooth reconstruction

In this paper we propose an interpolation algorithm using a mathematical morphology morphing approach. The aim of this algorithm is to reconstruct the $n$-dimensional object from a group of (n-1)-dimensional sets representing sections of that object. The morphing transformation modifies pairs of consecutive sets such that they approach in shape and size. The interpolated set is achieved when the two consecutive sets are made idempotent by the morphing transformation. We prove the convergence of the morphological morphing. The entire object is modeled by successively interpolating a certain number of intermediary sets between each two consecutive given sets. We apply the interpolation algorithm for 3-D tooth reconstruction

Perpendicular transport properties of YBa_2Cu_3O_{7-\delta}/PrBa_2Cu_3O_{7-\delta} superlattices

The coupling between the superconducting planes of YBa2Cu3O{7-\delta}/ PrBa2Cu3O{7-\delta} superlattices has been measured by c-axis transport. We show that only by changing the thickness of the superconducting YBa2Cu3O{7-\delta} layers, it is possible to switch between quasi-particle and Josephson tunneling. From our data we deduce a low temperature c-axis coherence length of 0.27 nm.Comment: Presented at LT22, contains 2 pages and 2 figures. to appear in Physica