Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Employee negative emotions in supervisory relationships : an attachment theory perspective

Relationships with supervisors are a major source of negative emotions at work, but little is known about why this is so. The aim of the research was to use attachment theory (Bowlby, 1969, 1973; 1980) as a framework for investigating the nature and causes of employee negative emotional experiences, in the context of their supervisory relationships. The research was conducted in three stages. In Stage 1 two studies were conducted to develop a measure of employee perceptions of supervisor caregiving (SCS). Results indicated that the 20-item scale had good reliability and validity. Stage 2 required participants (N=183) to complete a questionnaire that was designed to examine the roles of supervisor caregiving and working models (specific and global) in determining cognitive and emotional responses to hypothetical supervisor behaviours. The results provided partial support for an Independent Effects Model. Supervisor caregiving predicted specific anxiety and avoidance. In tum, both dimensions of attachment predicted negative emotions, but this relationship was mediated by event interpretation only in the case of avoidance. Global models made a smaller but significant contribution to negative emotions overall. There was no support for an interaction effect between specific and global models in determining event interpretation. In stage 3 a sub-sample of questionnaire respondents (N=24) were interviewed about 'real-life' caregiving and negative emotional experiences in their supervisory relationships. Secure individuals experienced supervisors as consistently warm, available, and responsive. They reported few negative events or emotions. Individuals with insecure specific working models experienced rejecting or inconsistent supervisor caregiving. They were sensitised to trust and closeness issues in their relationships, and reported negative events and emotions underpinned by these themes. Overall, results broadly supported attachment theory predictions. It is concluded that an attachment theory perspective provides new insight into the nature and causes of employee negative emotions in supervisory relationships.

Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

Antecedents of strategic flexibility:management cognition, firm resources and strategic options

Purpose: Current conceptualisations of strategic flexibility and its antecedents are theory-driven, which has resulted in a lack of consensus. To summarise this domain the paper aims to develop and present an a priori conceptual model of the antecedents and outcomes of strategic flexibility. Discussion and insights into the conceptual model, and the relationships specified, are made through a novel qualitative empirical approach. The implications for further research and a framework for further theoretical development are presented. Design/methodology/approach: An exploratory qualitative research design is used applying multiple data collection techniques in a branch network of a large regional retailer in the UK. The development of strategic options and the complex relationship to strategic flexibility is investigated. Findings: The number and type of strategic options developed by managers impact on the degree of strategic flexibility and also on the ability of the firm to achieve competitive differentiation. Additionally, the type of strategic option implemented by managers is dependent on the competitive situation faced at a local level. Evidence of managers' limited perception of competition was identified based on their spatial embeddedness. Research limitations/implications: A single, in-depth case study was used. The data gathered is rich and appropriate for the exploratory approach adopted here. However, generalisability of the findings is limited. Practical implications: Strategic flexibility is rooted in the ability of front-line mangers to develop and implement strategic options; this in turn facilitates competitive differentiation. Originality/value: The research presented is unique in this domain on two accounts. First, theory is developed by presenting an a priori conceptual model, and testing through in-depth qualitative data gathering. Second, insights into strategic flexibility are presented through an examination of managerial cognition, resources and strategic option generation using cognitive mapping and laddering technique. © Emerald Group Publishing Limited.

Developing the practice of educational psychologists through theory and research

Recent research in literacy acquisition has generated detailed programs for teaching phonological awareness. The current paper will address three issues that follow from this research. Firstly, much of the past research has been conducted under conditions that are divorced from the classroom. As a result, it is not known whether the suggested teaching strategies will lead to an increase in children’s attainments when integrated into a broad reading curriculum implemented by teachers in mainstream classrooms. Secondly, these phonological interventions have been designed either to prevent the occurrence of reading difficulties or to meet the needs of failing readers. Therefore, it is not known whether the same methods would advantage all children. Thirdly, teaching children to read takes a minimum of two to three academic years. We herefore need to develop a reading curriculum that can provide the progression and differentiation to meet a wide range of needs over several academic years. We report two studies that have addressed these issues through monitoring the impact of a reading curriculum, implemented by teachers, which integrated children’s acquisition of phonological skills with broader aspects of teaching reading over three academic years. The attainments of children at all levels of ability in the experimental group were raised relative to controls, and importantly, these gains were maintained after the intervention was withdrawn. These results demonstrate that phonological awareness training can be successfully integrated into real classroom contexts and that the same methods raised the attainments of normally developing children, as well as those at risk of reading failure.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Image enhancement of real-time television to benefit the visually impaired

Purpose: To examine the use of real-time, generic edge detection, image processing techniques to enhance the television viewing of the visually impaired. Design: Prospective, clinical experimental study. Method: One hundred and two sequential visually impaired (average age 73.8 ± 14.8 years; 59% female) in a single center optimized a dynamic television image with respect to edge detection filter (Prewitt, Sobel, or the two combined), color (red, green, blue, or white), and intensity (one to 15 times) of the overlaid edges. They then rated the original television footage compared with a black-and-white image displaying the edges detected and the original television image with the detected edges overlaid in the chosen color and at the intensity selected. Footage of news, an advertisement, and the end of program credits were subjectively assessed in a random order. Results: A Prewitt filter was preferred (44%) compared with the Sobel filter (27%) or a combination of the two (28%). Green and white were equally popular for displaying the detected edges (32%), with blue (22%) and red (14%) less so. The average preferred edge intensity was 3.5 ± 1.7 times. The image-enhanced television was significantly preferred to the original (P < .001), which in turn was preferred to viewing the detected edges alone (P < .001) for each of the footage clips. Preference was not dependent on the condition causing visual impairment. Seventy percent were definitely willing to buy a set-top box that could achieve these effects for a reasonable price. Conclusions: Simple generic edge detection image enhancement options can be performed on television in real-time and significantly enhance the viewing of the visually impaired. © 2007 Elsevier Inc. All rights reserved.

Metastable de-excitation spectroscopy and density functional theory study of the selective oxidation of crotyl alcohol over Pd(111)

The extremely surface sensitive technique of metastable de-excitation spectroscopy (MDS) has been utilized to probe the bonding and reactivity of crotyl alcohol over Pd(111) and provide insight into the selective oxidation pathway to crotonaldehyde. Auger de-excitation (AD) of metastable He (23S) atoms reveals distinct features associated with the molecular orbitals of the adsorbed alcohol, corresponding to emission from the hydrocarbon skeleton, the O n nonbonding, and C═C π states. The O n and C═C π states of the alcohol are reversed when compared to those of the aldehyde. Density functional theory (DFT) calculations of the alcohol show that an adsorption mode with both C═C and O bonds aligned somewhat parallel to the surface is energetically favored at a substrate temperature below 200 K. Density of states calculations for such configurations are in excellent agreement with experimental MDS measurements. MDS revealed oxidative dehydrogenation of crotyl alcohol to crotonaldehyde between 200 and 250 K, resulting in small peak shifts to higher binding energy. Intramolecular changes lead to the opposite assignment of the first two MOs in the alcohol versus the aldehyde, in accordance with DFT and UPS studies of the free molecules. Subsequent crotonaldehyde decarbonylation and associated propylidyne formation above 260 K could also be identified by MDS and complementary theoretical calculations as the origin of deactivation and selectivity loss. Combining MDS and DFT in this way represents a novel approach to elucidating surface catalyzed reaction pathways associated with a “real-world” practical chemical transformation, namely the selective oxidation of alcohols to aldehydes.

24-Karat or fool’s gold? Consequences of real team and co-acting group membership in healthcare organizations

Although theory on team membership is emerging, limited empirical attention has been paid to the effects of different types of team membership on outcomes. We propose that an important but overlooked distinction is that between membership of real teams and membership of co-acting groups, with the former being characterized by members who report that their teams have shared objectives, and structural interdependence and engage in team reflexivity. We hypothesize that real team membership will be associated with more positive individual- and organizational-level outcomes. These predictions were tested in the English National Health Service, using data from 62,733 respondents from 147 acute hospitals. The results revealed that individuals reporting the characteristics of real team membership, in comparison with those reporting the characteristics of co-acting group membership, witnessed fewer errors and incidents, experienced fewer work related injuries and illness, were less likely to be victims of violence and harassment, and were less likely to intend to leave their current employment. At the organizational level, hospitals with higher proportions of staff reporting the characteristics of real team membership had lower levels of patient mortality and sickness absence. The results suggest the need to clearly delineate real team membership in order to advance scientific understanding of the processes and outcomes of organizational teamwork.