A continuous-time quantum walk kernel for unattributed graphs

Kernel methods provide a way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. In this paper, we propose a novel kernel on unattributed graphs where the structure is characterized through the evolution of a continuous-time quantum walk. More precisely, given a pair of graphs, we create a derived structure whose degree of symmetry is maximum when the original graphs are isomorphic. With this new graph to hand, we compute the density operators of the quantum systems representing the evolutions of two suitably defined quantum walks. Finally, we define the kernel between the two original graphs as the quantum Jensen-Shannon divergence between these two density operators. The experimental evaluation shows the effectiveness of the proposed approach. © 2013 Springer-Verlag.

An adaptive hierarchical approach to the extraction of high resolution medial surfaces

We introduce a novel algorithm for medial surfaces extraction that is based on the density-corrected Hamiltonian analysis. The approach extracts the skeleton directly from a triangulated mesh and adopts an adaptive octree-based approach in which only skeletal voxels are refined to a lower level of the hierarchy, resulting in robust and accurate skeletons at extremely high resolution. The quality of the extracted medial surfaces is confirmed by an extensive set of experiments. © 2012 IEEE.

Exploring mapping-based visualisations of large remote image databases

Image database visualisations, in particular mapping-based visualisations, provide an interesting approach to accessing image repositories as they are able to overcome some of the drawbacks associated with retrieval based approaches. However, making a mapping-based approach work efficiently on large remote image databases, has yet to be explored. In this paper, we present Web-Based Images Browser (WBIB), a novel system that efficiently employs image pyramids to reduce bandwidth requirements so that users can interactively explore large remote image databases. © 2013 Authors.

Adaptive power and rate control for QoS guarantee of DSRC based collaborative road safety applications

In this paper we propose an adaptive power and message rate control method for safety applications at road intersections. The design objectives are to firstly provide guaranteed QoS support to both high priority emergency safety applications and low priority routine safety applications and secondly maximize channel utilization. We use an offline simulation based approach to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network with certain safety QoS requirements. The identified configurations are then used online by roadside access points (AP) adaptively according to estimated number of vehicles. Simulation results show that this adaptive method could provide required QoS support to safety applications and it significantly outperforms a fixed control method. © 2013 International Information Institute.

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.

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

Optimization of transit transfer times: A system-wide approach

This dissertation presents a system-wide approach, based on genetic algorithms, for the optimization of transfer times for an entire bus transit system. Optimization of transfer times in a transit system is a complicated problem because of the large set of binary and discrete values involved. The combinatorial nature of the problem imposes a computational burden and makes it difficult to solve by classical mathematical programming methods. ^ The genetic algorithm proposed in this research attempts to find an optimal solution for the transfer time optimization problem by searching for a combination of adjustments to the timetable for all the routes in the system. It makes use of existing scheduled timetables, ridership demand at all transfer locations, and takes into consideration the randomness of bus arrivals. ^ Data from Broward County Transit are used to compute total transfer times. The proposed genetic algorithm-based approach proves to be capable of producing substantial time savings compared to the existing transfer times in a reasonable amount of time. ^ The dissertation also addresses the issues related to spatial and temporal modeling, variability in bus arrival and departure times, walking time, as well as the integration of scheduling and ridership data. ^

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

A probabilistic approach to assess hydrate formation and design preventive measures

Formation of hydrates is one of the major flow assurance problems faced by the oil and gas industry. Hydrates tend to form in natural gas pipelines with the presence of water and favorable temperature and pressure conditions, generally low temperatures and corresponding high pressures. Agglomeration of hydrates can result in blockage of flowlines and equipment, which can be time consuming to remove in subsea equipment and cause safety issues. Natural gas pipelines are more susceptible to burst and explosion owing to hydrate plugging. Therefore, a rigorous risk-assessment related to hydrate formation is required, which assists in preventing hydrate blockage and ensuring equipment integrity. This thesis presents a novel methodology to assess the probability of hydrate formation and presents a risk-based approach to determine the parameters of winterization schemes to avoid hydrate formation in natural gas pipelines operating in Arctic conditions. It also presents a lab-scale multiphase flow loop to study the effects of geometric and hydrodynamic parameters on hydrate formation and discusses the effects of geometric and hydrodynamic parameters on multiphase development length of a pipeline. Therefore, this study substantially contributes to the assessment of probability of hydrate formation and the decision making process of winterization strategies to prevent hydrate formation in Arctic conditions.

Habitat map of Danajon Bank, Philippines, derived from a high-spatial-resolution multi-spectral satellite image and georeferenced point intercept transect and spot-check survey field data, using an object-based image classification method

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

Design, fabrication and characterization of resonant waveguide grating based optical biosensors

The absence of rapid, low cost and highly sensitive biodetection platform has hindered the implementation of next generation cheap and early stage clinical or home based point-of-care diagnostics. Label-free optical biosensing with high sensitivity, throughput, compactness, and low cost, plays an important role to resolve these diagnostic challenges and pushes the detection limit down to single molecule. Optical nanostructures, specifically the resonant waveguide grating (RWG) and nano-ribbon cavity based biodetection are promising in this context. The main element of this dissertation is design, fabrication and characterization of RWG sensors for different spectral regions (e.g. visible, near infrared) for use in label-free optical biosensing and also to explore different RWG parameters to maximize sensitivity and increase detection accuracy. Design and fabrication of the waveguide embedded resonant nano-cavity are also studied. Multi-parametric analyses were done using customized optical simulator to understand the operational principle of these sensors and more important the relationship between the physical design parameters and sensor sensitivities. Silicon nitride (SixNy) is a useful waveguide material because of its wide transparency across the whole infrared, visible and part of UV spectrum, and comparatively higher refractive index than glass substrate. SixNy based RWGs on glass substrate are designed and fabricated applying both electron beam lithography and low cost nano-imprint lithography techniques. A Chromium hard mask aided nano-fabrication technique is developed for making very high aspect ratio optical nano-structure on glass substrate. An aspect ratio of 10 for very narrow (~60 nm wide) grating lines is achieved which is the highest presented so far. The fabricated RWG sensors are characterized for both bulk (183.3 nm/RIU) and surface sensitivity (0.21nm/nm-layer), and then used for successful detection of Immunoglobulin-G (IgG) antibodies and antigen (~1μg/ml) both in buffer and serum. Widely used optical biosensors like surface plasmon resonance and optical microcavities are limited in the separation of bulk response from the surface binding events which is crucial for ultralow biosensing application with thermal or other perturbations. A RWG based dual resonance approach is proposed and verified by controlled experiments for separating the response of bulk and surface sensitivity. The dual resonance approach gives sensitivity ratio of 9.4 whereas the competitive polarization based approach can offer only 2.5. The improved performance of the dual resonance approach would help reducing probability of false reading in precise bio-assay experiments where thermal variations are probable like portable diagnostics.

Telescoped approach to aryl hydroxymethylation in the synthesis of a key pharmaceutical intermediate

An efficient synthetic approach leading to introduction of the hydroxymethyl group to an aryl moiety via combination of the Bouveault formylation and hydride reduction has been optimized using a rational, mechanistic-based approach. This approach enabled telescoping of the two steps into a single efficient process, readily amenable to scaleup.

Reputation performance: a portfolio selection approach

This paper introduces a normative view on corporate reputation strategic management. Reputation performance is conceptualised as the outcome of complex processes and social interactions and the lack of a holistic reputation performance management framework is identified. In an attempt to fill this gap, a portfolio-based approach is put forward. Drawing on the foundations of modern portfolio theory we create a portfolio-based reputation management algorithmic model where reputation components and priorities are weighted by decision makers and shape organisational change in an attempt to formulate a corporate reputation strategy. The rationale of this paper is based on the foundational consideration of organisations as choosing he optimal strategy by seeking to maximise their reputation performance while maintaining organisational stability and minimising organisational risk.

Reputation Performance: a portfolio selection approach

This paper introduces a normative view on corporate reputation management; an algorithmic model for reputation-driven strategic decision making is proposed and corporate reputation is conceptualized as influenced by a selection among organizational priorities. A portfolio-based approach is put forward; we draw on the foundations of portfolio theory and we create a portfolio-based reputation management model where reputation components and priorities are weighted by decision makers and shape organizational change in an attempt to formulate a corporate reputation strategy. The rationale of this paper is based on the foundational consideration of organizations as choosing the optimal strategy by seeking to maximize performance on corporate reputation capital while maintaining organizational stability and minimizing organizational risk.