Manual asymmetries in the preparation and control of goal-directed movements

The primary purpose of this experiment was to determine if left hand reaction time advantages in manual aiming result from a right hemisphere attentional advantage or an early right hemisphere role in movement preparation. Right-handed participants were required to either make rapid goal-directed movements to small targets or simply lift their hand upon target illumination. The amount of advance information about the target for a particular trial was manipulated by precuing a subset of potential targets prior to the reaction time interval. When participants were required to make aiming movements to targets in left space, the left hand enjoyed a reaction advantage that was not present for aiming in right space: or simple finger lifts. This advantage was independent of the amount or type of advance information provided by the precue. This finding supports the movement planning hypothesis. With respect to movement execution, participants completed their aiming movements more quickly when aiming with their right hand, particularly in right space. This right hand advantage in right space was due to the time required to decelerate the movement and to make feedback-based adjustments late in the movement trajectory. (C) 2001 Academic Press.

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

Metal-Directed Synthesis of Enantiomerially Pure Dimetallic Lanthanide Luminescent Triple-Stranded Helicates

The synthesis and photophysical evaluation of two enatiomerially pure dimetallic lanthanide luminescent triple-stranded helicates is described. The two systems, formed from the chiral (R,R) ligand 1 and (S,S) ligand 2, were produced as single species in solution, where the excitation of either the naphthalene antennae or the pyridiyl units gave rise to Eu(III) emission in a variety of solvents. Excitation of the antennae also gave rise to circularly polarized Eu(III) luminescence emissions for Eu2:13 and Eu2:23 that were of equal intensity and opposite sign, confirming their enantiomeric nature in solution providing a basis upon which we were able to assign the absolute configurations of Eu2:13 and Eu2:23.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.

Compact Toffoli gate using weighted graph states

We introduce three compact graph states that can be used to perform a measurement-based Toffoli gate. Given a weighted graph of six, seven, or eight qubits, we show that success probabilities of 1/4, 1/2, and 1, respectively, can be achieved. Our study puts a measurement-based version of this important quantum logic gate within the reach of current experiments. As the graphs are setup independent, they could be realized in a variety of systems, including linear optics and ion traps.

Carbon leakage revisited: unilateral climate policy with directed technical change

Using a stylized theoretical model, we argue that current economic analyses of climate policy tend to over-estimate the degree of carbon leakage, as they abstract from the effects of induced technological change. We analyse carbon leakage in a two-country model with directed technical change, where only one of the countries enforces an exogenous cap on emissions. Climate policy induces changes in relative prices, that cause carbon leakage through a terms-of-trade effect. However, these changes in relative prices also affect the incentives to innovate in different sectors. This leads to a counterbalancing induced-technology effect, which always reduces carbon leakage. We therefore conclude that the leakage rates reported in the literature may be too high, as these estimates neglect the effect of price changes on the incentives to innovate.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.