Changing state disruption of lip-reading by irrelevant sound in perceptual and memory tasks

Three experiments investigated irrelevant sound interference of lip-read lists. In Experiment 1, an acoustically changing sequence of nine irrelevant utterances was more disruptive to spoken immediate identification of lists of nine lip-read digits than nine repetitions of the same utterances (the changing-state effect; Jones, Madden, & Miles, 1992). Experiment 2 replicated this finding when lip-read items were sampled with replacement from the nine digits to form the lip-read lists. In Experiment 3, when the irrelevant sound was confined to the retention interval of a delayed recall task, a changing-state pattern of disruption also occurred. Results confirm a changing-state effect in memory for lip-read items but also point to the possibility that, for lip-reading, changing-state effects may occur at an earlier, perceptual stage.

Auditory memory and the irrelevant sound effect: further evidence for changing state disruption

Four experiments investigate the hypothesis that irrelevant sound interferes with serial recall of auditory items in the same fashion as with visually presented items. In Experiment 1 an acoustically changing sequence of 30 irrelevant utterances was more disruptive than 30 repetitions of the same utterance (the changing-state effect; Jones, Madden, & Miles, 1992) whether the to-be-remembered items were visually or auditorily presented. Experiment 2 showed that two different utterances spoken once (a heterogeneous compound suffix; LeCompte & Watkins, 1995) produced less disruption to serial recall than 15 repetitions of the same sequence. Disruption thus depends on the number of sounds in the irrelevant sequence. In Experiments 3a and 3b the number of different sounds, the "token-set" size (Tremblay & Jones, 1998), in an irrelevant sequence also influenced the magnitude of disruption in both irrelevant sound and compound suffix conditions. The results support the view that the disruption of memory for auditory items, like memory for visually presented items, is dependent on the number of different irrelevant sounds presented and the size of the set from which these sounds are taken. Theoretical implications are discussed.

A left-ear disadvantage for the presentation of irrelevant sound: manipulations of task requirements and changing state

Three experiments attempted to clarify the effect of altering the spatial presentation of irrelevant auditory information. Previous research using serial recall tasks demonstrated a left-ear disadvantage for the presentation of irrelevant sounds (Hadlington, Bridges, & Darby, 2004). Experiments 1 and 2 examined the effects of manipulating the location of irrelevant sound on either a mental arithmetic task (Banbury & Berry, 1998) or a missing-item task (Jones & Macken, 1993; Experiment 4). Experiment 3 altered the amount of change in the irrelevant stream to assess how this affected the level of interference elicited. Two prerequisites appear necessary to produce the left-ear disadvantage; the presence of ordered structural changes in the irrelevant sound and the requirement for serial order processing of the attended information. The existence of a left-ear disadvantage highlights the role of the right hemisphere in the obligatory processing of auditory information. (c) 2006 Published by Elsevier Inc.

Landscape state machines: Tools for evolutionary algorithm performance analyses and landscape/algorithm mapping

Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typically be needed) or both. In such circumstances, there is a dire need to tune various features of the algorithm well so that performance and time savings are optimized. However, these are precisely the circumstances in which prior tuning is very costly in time and resources. There is hence a need for methods which enable fast prior tuning in such cases. We describe a candidate technique for this purpose, in which we model a landscape as a finite state machine, inferred from preliminary sampling runs. In prior algorithm-tuning trials, we can replace the 'real' landscape with the model, enabling extremely fast tuning, saving far more time than was required to infer the model. Preliminary results indicate much promise, though much work needs to be done to establish various aspects of the conditions under which it can be most beneficially used. A main limitation of the method as described here is a restriction to mutation-only algorithms, but there are various ways to address this and other limitations.

Mapping evidence gaps in anti-corruption: assessing the state of the operationally relevant evidence on donors' actions and approaches to reducing corruption

This paper charts the current evidence on effectiveness of different anti-corruption reforms, and identifies significant evidence gaps. Despite a substantial amount of literature on corruption, this review found very few studies focusing on anti-corruption reforms, and even fewer that credibly assess issues of effectiveness and impact. The evidence was strong for only two types of interventions: public financial management (PFM) reforms and supreme audit institutions (SAIs). For PFM, the evidence in general showed positive results, whereas the effectiveness was mixed for SAIs. No strong evidence indicates that any of the interventions pursued have been ineffective, but there is fair evidence that anti-corruption authorities, civil service reforms and the use of corruption conditionality in aid allocation decisions in general have not been effective. The paper advocates more operationally-relevant research and rigorous evaluations to build up the missing evidence base, particularly in conflict-afflicted states, in regards to the private sector, and on the interactions and interdependencies between different anti-corruption interventions.

Why we need new spectacles: Mapping the experiential dimension in prehistoric Cretan landscapes

The Bronze to Iron Age transition in Crete, a period of state collapse and insecurity, saw the island's rugged, high-contrast topography used in striking new ways. The visual drama of many of the new site locations has stimulated significant research over the last hundred years, with explanation of the change as the main focus. The new sites are not monumental in character: the vast majority are settlements, and much of the information about them comes from survey. Perhaps as a result, the new site map has not been much studied from phenomenological perspectives. A focus on the visual and experiential aspects of the new landscape can offer valuable insights into social structures at this period, and illuminate social developments prefiguring the emergence of polis states in Crete by c. 700 BC. To develop, share and evaluate this type of integrated study, digital reconstructive techniques are still under-used in this region. I highlight their potential value in addressing a regularly-identified shortcoming of phenomenological approaches-their necessarily subjective emphasis.

Why we need new spectacles: Mapping the experiential dimension in prehistoric Cretan landscapes

The Bronze to Iron Age transition in Crete, a period of state collapse and insecurity, saw the island's rugged, high-contrast topography used in striking new ways. The visual drama of many of the new site locations has stimulated significant research over the last hundred years, with explanation of the change as the main focus. The new sites are not monumental in character: the vast majority are settlements, and much of the information about them comes from survey. Perhaps as a result, the new site map has not been much studied from phenomenological perspectives. A focus on the visual and experiential aspects of the new landscape can offer valuable insights into social structures at this period, and illuminate social developments prefiguring the emergence of polis states in Crete by c. 700 BC. To develop, share and evaluate this type of integrated study, digital reconstructive techniques are still under-used in this region. I highlight their potential value in addressing a regularly-identified shortcoming of phenomenological approaches-their necessarily subjective emphasis.

Mapping and controlling molecular processes: The application of control theory and system identification algorithms to ASOPS and other fast pulse spectroscopies

Asynchronous Optical Sampling (ASOPS) [1,2] and frequency comb spectrometry [3] based on dual Ti:saphire resonators operated in a master/slave mode have the potential to improve signal to noise ratio in THz transient and IR sperctrometry. The multimode Brownian oscillator time-domain response function described by state-space models is a mathematically robust framework that can be used to describe the dispersive phenomena governed by Lorentzian, Debye and Drude responses. In addition, the optical properties of an arbitrary medium can be expressed as a linear combination of simple multimode Brownian oscillator functions. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing the recorded THz transients in the time or frequency domain will be outlined [4,5]. Since a femtosecond duration pulse is capable of persistent excitation of the medium within which it propagates, such approach is perfectly justifiable. Several de-noising routines based on system identification will be shown. Furthermore, specifically developed apodization structures will be discussed. These are necessary because due to dispersion issues, the time-domain background and sample interferograms are non-symmetrical [6-8]. These procedures can lead to a more precise estimation of the complex insertion loss function. The algorithms are applicable to femtosecond spectroscopies across the EM spectrum. Finally, a methodology for femtosecond pulse shaping using genetic algorithms aiming to map and control molecular relaxation processes will be mentioned.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.

State-space model identification of a wireless THz network

We model the large scale fading of wireless THz communications links deployed in a metropolitan area taking into account reception through direct line of sight, ground or wall reflection and diffraction. The movement of the receiver in the three dimensions is modelled by an autonomous dynamic linear system in state-space whereas the geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a Wiener model from time-domain measurements of the field intensity.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

The British state and the Irish rebellion of 1916: an intelligence failure or an failure of response

The teleological narrative that has dominated the handling of intelligence by the British state in the events that led up to the 1916 Irish Rebellion in Dublin has been characterised as a cocktail of incompetence and mendacity. Using new and existing archive material this article argues that both the cabinet in London and key members of the Irish Executive in Dublin were supplied with accurate and timely intelligence by the Admiralty's signals intelligence unit, the Royal Irish Constabulary and the Dublin Metropolitan Police with respect to this event. Far from being a failure of intelligence here is evidence to show that there occurred a failure of response on behalf of key decision-makers. The warnings that were given by intelligence organisations were filtered through the existing policy preferences and assumptions. As a result of these factors accurate evaluations and sound judgement were not exercised by key officials, such as Sir Matthew Nathan, in Dublin Castle.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.