Added luminance ramp alters perceived edge blur and contrast:A critical test for derivative-based models of edge coding

In many models of edge analysis in biological vision, the initial stage is a linear 2nd derivative operation. Such models predict that adding a linear luminance ramp to an edge will have no effect on the edge's appearance, since the ramp has no effect on the 2nd derivative. Our experiments did not support this prediction: adding a negative-going ramp to a positive-going edge (or vice-versa) greatly reduced the perceived blur and contrast of the edge. The effects on a fairly sharp edge were accurately predicted by a nonlinear multi-scale model of edge processing [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision], in which a half-wave rectifier comes after the 1st derivative filter. But we also found that the ramp affected perceived blur more profoundly when the edge blur was large, and this greater effect was not predicted by the existing model. The model's fit to these data was much improved when the simple half-wave rectifier was replaced by a threshold-like transducer [May, K. A. & Georgeson, M. A. (2007). Blurred edges look faint, and faint edges look sharp: The effect of a gradient threshold in a multi-scale edge coding model. Vision Research, 47, 1705-1720.]. This modified model correctly predicted that the interaction between ramp gradient and edge scale would be much larger for blur perception than for contrast perception. In our model, the ramp narrows an internal representation of the gradient profile, leading to a reduction in perceived blur. This in turn reduces perceived contrast because estimated blur plays a role in the model's estimation of contrast. Interestingly, the model predicts that analogous effects should occur when the width of the window containing the edge is made narrower. This has already been confirmed for blur perception; here, we further support the model by showing a similar effect for contrast perception. © 2007 Elsevier Ltd. All rights reserved.

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.

Self-explanation in adaptive systems based on runtime goal-based models

The behaviour of self adaptive systems can be emergent, which means that the system’s behaviour may be seen as unexpected by its customers and its developers. Therefore, a self-adaptive system needs to garner confidence in its customers and it also needs to resolve any surprise on the part of the developer during testing and maintenance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive system’s behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, we propose the use of goal-based requirements models at runtime to offer self-explanation of how a system is meeting its requirements. We demonstrate the analysis of run-time requirements models to yield a self-explanation codified in a domain specific language, and discuss possible future work.

20-km distributed temperature sensor based on spontaneous Brillouin scattering

We report on a novel experimental setup for distributed measurement of temperature, based on spontaneous Brillouin scattering in optical fiber. We have developed a mode-locked Brillouin fiber ring laser in order to generate the dual frequency source required for a heterodyne detection of the backscattered signal. This relatively simple system enables temperature measurements over 20 km with a spatial resolution of 7 m.

20-km distributed temperature sensor based on spontaneous Brillouin scattering

We report on a novel experimental setup for distributed measurement of temperature, based on spontaneous Brillouin scattering in optical fiber. We have developed a mode-locked Brillouin fiber ring laser in order to generate the dual frequency source required for a heterodyne detection of the backscattered signal. This relatively simple system enables temperature measurements over 20 km with a spatial resolution of 7 m.

Single uniform FBG for simultaneous measurement of liquid level and temperature

In this paper, we propose and demonstrate a novel scheme for simultaneous measurement of liquid level and temperature based on a simple uniform fiber Bragg grating (FBG) by monitoring both the short-wavelength-loss peaks and its Bragg resonance. The liquid level can be measured from the amplitude changes of the short-wavelength-loss peaks, while temperature can be measured from the wavelength shift of the Bragg resonance. Both theoretical simulation results and experimental results are presented. Such a scheme has some advantages including robustness, simplicity, flexibility in choosing sensitivity and simultaneous temperature measurement capability.

Forecasting the UK/US exchange rate with divisia monetary models and neural networks

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative merits of the official Simple Sum and the weighted Divisia measures of money. Overall, there are four main findings. First, the majority of the models with fundamentals are able to beat the RW model in forecasting the UK/US exchange rate. Second, the most accurate forecasts of the UK/US exchange rate are obtained with a nonlinear model. Third, taking into account structural breaks reveals that the Divisia aggregate performs better than its Simple Sum counterpart. Finally, Divisia-based models provide more accurate forecasts than Simple Sum-based models provided they are constructed within a nonlinear framework.

Single uniform FBG for simultaneous measurement of liquid level and temperature

In this paper, we propose and demonstrate a novel scheme for simultaneous measurement of liquid level and temperature based on a simple uniform fiber Bragg grating (FBG) by monitoring both the short-wavelength-loss peaks and its Bragg resonance. The liquid level can be measured from the amplitude changes of the short-wavelength-loss peaks, while temperature can be measured from the wavelength shift of the Bragg resonance. Both theoretical simulation results and experimental results are presented. Such a scheme has some advantages including robustness, simplicity, flexibility in choosing sensitivity and simultaneous temperature measurement capability.

Role of fluctuations in membrane models:thermal versus nonthermal

We study the comparative importance of thermal to nonthermal fluctuations for membrane-based models in the linear regime. Our results, both in 1+1 and 2+1 dimensions, suggest that nonthermal fluctuations dominate thermal ones only when the relaxation time τ is large. For moderate to small values of τ, the dynamics is defined by a competition between these two forces. The results are expected to act as a quantitative benchmark for biological modeling in systems involving cytoskeletal and other nonthermal fluctuations. © 2011 American Physical Society.

Microfiber-based inline Mach-Zehnder interferometer for dual-parameter measurement

An approach to realizing simultaneous measurement of refractive index (RI) and temperature based on a microfiber-based dual inline Mach-Zehnder interferometer (MZI) is proposed and demonstrated. Due to different interference mechanisms, as one interference between the core mode and the lower order cladding mode in the sensing single-mode fiber and the other interference between the fundamental mode and the high-order mode in the multimode microfiber, the former interferometer achieves RI sensitivity of -23.67 nm/RIU and temperature sensitivity of 81.2 pm/oC, whereas those of the latter are 3820.23 nm/RIU, and -465.7 pm/oC, respectively. The large sensitivity differences can provide a more accurate demodulation of RI and temperature. The sensor is featured with multiparameters measurement, compact structure, high sensitivity, low cost, and easy fabrication.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Implementing an agent-based model with a spatial visual display in discrete-event simulation software

There has been an increasing interest in the use of agent-based simulation and some discussion of the relative merits of this approach as compared to discrete-event simulation. There are differing views on whether an agent-based simulation offers capabilities that discrete-event cannot provide or whether all agent-based applications can at least in theory be undertaken using a discrete-event approach. This paper presents a simple agent-based NetLogo model and corresponding discrete-event versions implemented in the widely used ARENA software. The two versions of the discrete-event model presented use a traditional process flow approach normally adopted in discrete-event simulation software and also an agent-based approach to the model build. In addition a real-time spatial visual display facility is provided using a spreadsheet platform controlled by VBA code embedded within the ARENA model. Initial findings from this investigation are that discrete-event simulation can indeed be used to implement agent-based models and with suitable integration elements such as VBA provide the spatial displays associated with agent-based software.

Blurred edges look faint, and faint edges look sharp:The effect of a gradient threshold in a multi-scale edge coding model

A multi-scale model of edge coding based on normalized Gaussian derivative filters successfully predicts perceived scale (blur) for a wide variety of edge profiles [Georgeson, M. A., May, K. A., Freeman, T. C. A., & Hesse, G. S. (in press). From filters to features: Scale-space analysis of edge and blur coding in human vision. Journal of Vision]. Our model spatially differentiates the luminance profile, half-wave rectifies the 1st derivative, and then differentiates twice more, to give the 3rd derivative of all regions with a positive gradient. This process is implemented by a set of Gaussian derivative filters with a range of scales. Peaks in the inverted normalized 3rd derivative across space and scale indicate the positions and scales of the edges. The edge contrast can be estimated from the height of the peak. The model provides a veridical estimate of the scale and contrast of edges that have a Gaussian integral profile. Therefore, since scale and contrast are independent stimulus parameters, the model predicts that the perceived value of either of these parameters should be unaffected by changes in the other. This prediction was found to be incorrect: reducing the contrast of an edge made it look sharper, and increasing its scale led to a decrease in the perceived contrast. Our model can account for these effects when the simple half-wave rectifier after the 1st derivative is replaced by a smoothed threshold function described by two parameters. For each subject, one pair of parameters provided a satisfactory fit to the data from all the experiments presented here and in the accompanying paper [May, K. A. & Georgeson, M. A. (2007). Added luminance ramp alters perceived edge blur and contrast: A critical test for derivative-based models of edge coding. Vision Research, 47, 1721-1731]. Thus, when we allow for the visual system's insensitivity to very shallow luminance gradients, our multi-scale model can be extended to edge coding over a wide range of contrasts and blurs. © 2007 Elsevier Ltd. All rights reserved.

Investigation of aspects of reactive gliosis in human astrocytoma cell lines:application for toxicity assessment

The process of astrogliosis, or reactive gliosis, is a typical response of astrocytes to a wide range of physical and chemical injuries. The up-regulation of the astrocyte specific glial fibrillary acidic protein (GFAP) is a hallmark of reactive gliosis and is widely used as a marker to identify the response. In order to develop a reliable, sensitive and high throughput astrocyte toxicity assay that is more relevant to the human response than existing animal cell based models, the U251-MG, U373-MG and CCF-STTG 1 human astrocytoma cell lines were investigated for their ability to exhibit reactive-like changes following exposure to ethanol, chloroquine diphosphate, trimethyltin chloride and acrylamide. Cytotoxicity analysis showed that the astrocytic cells were generally more resistant to the cytotoxic effects of the agents than the SH-SY5Y neuroblastoma cells. Retinoic acid induced differentiation of the SH-SY5Y line was also seen to confer some degree of resistance to toxicant exposure, particularly in the case of ethanol. Using a cell based ELISA for GFAP together with concurrent assays for metabolic activity and cell number, each of the three cell lines responded to toxicant exposure by an increase in GFAP immunoreactivity (GFAP-IR), or by increased metabolic activity. Ethanol, chloroquine diphosphate, trimethyltin chloride and bacterial lipopolysaccharide all induced either GFAP or MTT increases depending upon the cell line, dose and exposure time. Preliminary investigations of additional aspects of astrocytic injury indicated that IL-6, but not TNF-α. or nitric oxide, is released following exposure to each of the compounds, with the exception of acrylamide. It is clear that these human astrocytoma cell lines are capable of responding to toxicant exposure in a manner typical of reactive gliosis and are therefore a valuable cellular model in the assessment of in vitro neurotoxicity.