Texture description and segmentation in image processing

Textured regions in images can be defined as those regions containing a signal which has some measure of randomness. This thesis is concerned with the description of homogeneous texture in terms of a signal model and to develop a means of spatially separating regions of differing texture. A signal model is presented which is based on the assumption that a large class of textures can adequately be represented by their Fourier amplitude spectra only, with the phase spectra modelled by a random process. It is shown that, under mild restrictions, the above model leads to a stationary random process. Results indicate that this assumption is valid for those textures lacking significant local structure. A texture segmentation scheme is described which separates textured regions based on the assumption that each texture has a different distribution of signal energy within its amplitude spectrum. A set of bandpass quadrature filters are applied to the original signal and the envelope of the output of each filter taken. The filters are designed to have maximum mutual energy concentration in both the spatial and spatial frequency domains thus providing high spatial and class resolutions. The outputs of these filters are processed using a multi-resolution classifier which applies a clustering algorithm on the data at a low spatial resolution and then performs a boundary estimation operation in which processing is carried out over a range of spatial resolutions. Results demonstrate a high performance, in terms of the classification error, for a range of synthetic and natural textures

Targets and self monitoring in hypertension: randomised controlled trial and cost effectiveness analysis

OBJECTIVES: To assess whether blood pressure control in primary care could be improved with the use of patient held targets and self monitoring in a practice setting, and to assess the impact of these on health behaviours, anxiety, prescribed antihypertensive drugs, patients' preferences, and costs. DESIGN: Randomised controlled trial. SETTING: Eight general practices in south Birmingham. PARTICIPANTS: 441 people receiving treatment in primary care for hypertension but not controlled below the target of < 140/85 mm Hg. INTERVENTIONS: Patients in the intervention group received treatment targets along with facilities to measure their own blood pressure at their general practice; they were also asked to visit their general practitioner or practice nurse if their blood pressure was repeatedly above the target level. Patients in the control group received usual care (blood pressure monitoring by their practice). MAIN OUTCOME MEASURES: Primary outcome: change in systolic blood pressure at six months and one year in both intervention and control groups. Secondary outcomes: change in health behaviours, anxiety, prescribed antihypertensive drugs, patients' preferences of method of blood pressure monitoring, and costs. RESULTS: 400 (91%) patients attended follow up at one year. Systolic blood pressure in the intervention group had significantly reduced after six months (mean difference 4.3 mm Hg (95% confidence interval 0.8 mm Hg to 7.9 mm Hg)) but not after one year (mean difference 2.7 mm Hg (- 1.2 mm Hg to 6.6 mm Hg)). No overall difference was found in diastolic blood pressure, anxiety, health behaviours, or number of prescribed drugs. Patients who self monitored lost more weight than controls (as evidenced by a drop in body mass index), rated self monitoring above monitoring by a doctor or nurse, and consulted less often. Overall, self monitoring did not cost significantly more than usual care (251 pounds sterling (437 dollars; 364 euros) (95% confidence interval 233 pounds sterling to 275 pounds sterling) versus 240 pounds sterling (217 pounds sterling to 263 pounds sterling). CONCLUSIONS: Practice based self monitoring resulted in small but significant improvements of blood pressure at six months, which were not sustained after a year. Self monitoring was well received by patients, anxiety did not increase, and there was no appreciable additional cost. Practice based self monitoring is feasible and results in blood pressure control that is similar to that in usual care.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Lay perceptions of type 2 diabetes in Scotland:bringing health services back in

The growing prevalence of type 2 diabetes is placing Scottish health services under considerable strain. Consequently, diabetes services are undergoing a major process of reorganisation, including the devolvement of routine diabetes care/diabetic review from secondary to primary healthcare settings. This qualitative study was devised to explore newly diagnosed type 2 diabetes patients' perceptions of their disease and the health services they receive at a time when this restructuring of services is taking place. The sample comprised 40 patients resident in Lothian, Scotland, who had diverse experiences of services, some receiving GP-based care only, others having varying contact with hospital diabetes clinics. In-depth interviews were undertaken with patients, three times at six monthly intervals over 1 year, enabling their experiences to be tracked at critical junctures during the post-diagnostic period. Disease perceptions and health service delivery were found to be mutually informing and effecting. Not only did (different types of) health service delivery influence the ways in which patients thought about and self-managed their disease, over time patients' disease perceptions also informed their expectations of, and preferences for, diabetes services. We thus argue that there is a need for a reconceptualisation within the medical social sciences to take into account the context of healthcare and the economic/policy factors that inform health service delivery when looking at patients' disease perceptions. We also discuss the logistical and ethical challenges of drawing upon patients' perspectives, preferences and views in the design and delivery of future health services. © 2004 Elsevier Ltd. All rights reserved.

Individual differences in occupational perceptions: a developmental study

This thesis presents an investigation of the structure of people's occupational perceptions. The questionnaires used In this study collected both descriptive information about people's perceptions of occupations and also pair comparison similarities data. The data were collected both in the United States of America and England from samples of subjects who differed in terms of age and sex. This provided, therefore, both cross-cultural and developmental dimensions to the study. A cognitive orientation to the study of vocational behaviour is developed and multidimensional scaling procedures are used to analyze the data. A prime concern of the thesis is to examine the appropriateness of this approach and these techniques to this subject area. The results of this study show that a considerable range of individuaI differences exist in occupational perceptions.0lder subjects have a more complex structure to their perceptions and showed greater consensus as to how they perceived occupations to relate to each other. Younger subjects exhibited a greater range of individual differences in occupational perceptions but had, on average, a simpler subjective occupational structure. The multidimensional scaling procedures used in this study were able to reveal how occupational perceptions were structured, to relate these occupational perceptions to occupational preferences and other evaluative data, and to show that the groupings and structure of occupational perceptions ore similar to the dimensions used in occupational classification schemes. ImpIications of these resultts to vocationaI guidance theory and practice are discussed. The resuIts reported here strongly support both the use of the cognitive approach adopted here and demonstrate the potential of multidimensional scaling techniques for further:research in the field of vocational psychology.

Individual differences in the sensory perception of fats

Excessive consumption of dietary fat is acknowledged to be a widespread problem linked to a range of medical conditions. Despite this, little is known about the specific sensory appeal held by fats and no previous published research exists concerning human perception of non-textural taste qualities in fats. This research aimed to address whether a taste component can be found in sensory perception of pure fats. It also examined whether individual differences existed in human taste responses to fat, using both aggregated data analysis methods and multidimensional scaling. Results indicated that individuals were able to detect both the primary taste qualities of sweet, salty, sour and bitter in pure processed oils and reliably ascribe their own individually-generated taste labels, suggested that a taste component may be present in human responses to fat. Individual variation appeared to exist, both in the perception of given taste qualities and in perceived intensity and preferences. A number of factors were examined in relation to such individual differences in taste perception, including age, gender, genetic sensitivity to 6-n-propylthiouracil, body mass, dietary preferences and intake, dieting behaviours and restraint. Results revealed that, to varying extents, gender, age, sensitivity to 6-n-propylthiouracil, dietary preferences, habitual dietary intake and restraint all appeared to be related to individual variation in taste responses to fat. However, in general, these differences appeared to exist in the form of differing preferences and levels of intensity with which taste qualities detected in fat were perceived, as opposed to the perception of specific taste qualities being associated with given traits or states. Equally, each of these factors appeared to exert only a limited influence upon variation in sensory responses and thus the potential for using taste responses to fats as a marker for issues such as over-consumption, obesity or eating disorder is at present limited.

Designing a model for a corpus-driven dictionary of academic English

University students encounter difficulties with academic English because of its vocabulary, phraseology, and variability, and also because academic English differs in many respects from general English, the language which they have experienced before starting their university studies. Although students have been provided with many dictionaries that contain some helpful information on words used in academic English, these dictionaries remain focused on the uses of words in general English. There is therefore a gap in the dictionary market for a dictionary for university students, and this thesis provides a proposal for such a dictionary (called the Dictionary of Academic English; DOAE) in the form of a model which depicts how the dictionary should be designed, compiled, and offered to students. The model draws on state-of-the-art techniques in lexicography, dictionary-use research, and corpus linguistics. The model demanded the creation of a completely new corpus of academic language (Corpus of Academic Journal Articles; CAJA). The main advantages of the corpus are its large size (83.5 million words) and balance. Having access to a large corpus of academic language was essential for a corpus-driven approach to data analysis. A good corpus balance in terms of domains enabled a detailed domain-labelling of senses, patterns, collocates, etc. in the dictionary database, which was then used to tailor the output according to the needs of different types of student. The model proposes an online dictionary that is designed as an online dictionary from the outset. The proposed dictionary is revolutionary in the way it addresses the needs of different types of student. It presents students with a dynamic dictionary whose contents can be customised according to the user's native language, subject of study, variant spelling preferences, and/or visual preferences (e.g. black and white).

Mean-variance Vs. full-scale optimization: broad evidence for the UK

This article investigates the performance of a model called Full-Scale Optimisation, which was presented recently and is used for financial investment advice. The investor’s preferences of expected risk and return are entered into the model, and a recommended portfolio is produced. This model is theoretically more accurate than the mainstream investment advice model, called Mean-Variance Optimization, as there are fewer assumptions made. Our investigation of the model’s performance is broader when it comes to investor preferences, and more general when it comes to investment type, as compared to previous studies. Our investigation shows that Full-Scale Optimisation is more widely applicable than earlier known.

Embodied myopia

One field study and five experiments show that seemingly irrelevant bodily actions influence consumer behavior. These studies demonstrate that arm flexion (in which the motor action is directed toward the self) versus arm extension (in which the motor action is directed away from the self) influences purchase behavior, product preferences, and economic decisions. More specifically, arm flexion increases the likelihood of purchasing vice products (Study 1a), leads to a preference for vices over virtues (Studies 1b and 2a), and leads to preference for smaller, sooner over larger, later monetary rewards (Studies 2b, 3, and 4). The authors argue that arm flexion induces present-biased preferences through activation of approach motivation. The effect of bodily actions on present-biased preferences is regulated by the behavioral approach system (Studies 3 and 4) and relies on the learned association between arm flexion and activation of this approach system (Study 4). The authors discuss implications for Intertemporal decision making, embodied cognition, and marketing practice. © 2011, American Marketing Association.

Investigating G protein signalling bias at the glucagon-like peptide-1 receptor in yeast

Background and Purpose The glucagon-like peptide 1 (GLP-1) receptor performs an important role in glycaemic control, stimulating the release of insulin. It is an attractive target for treating type 2 diabetes. Recently, several reports of adverse side effects following prolonged use of GLP-1 receptor therapies have emerged: most likely due to an incomplete understanding of signalling complexities. Experimental Approach We describe the expression of the GLP-1 receptor in a panel of modified yeast strains that couple receptor activation to cell growth via single Gα/yeast chimeras. This assay enables the study of individual ligand-receptor G protein coupling preferences and the quantification of the effect of GLP-1 receptor ligands on G protein selectivity. Key Results The GLP-1 receptor functionally coupled to the chimeras representing the human Gαs, Gαi and Gαq subunits. Calculation of the dissociation constant for a receptor antagonist, exendin-3 revealed no significant difference between the two systems. We obtained previously unobserved differences in G protein signalling bias for clinically relevant therapeutic agents, liraglutide and exenatide; the latter displaying significant bias for the Gαi pathway. We extended the use of the system to investigate small-molecule allosteric compounds and the closely related glucagon receptor. Conclusions and Implications These results provide a better understanding of the molecular events involved in GLP-1 receptor pleiotropic signalling and establish the yeast platform as a robust tool to screen for more selective, efficacious compounds acting at this important class of receptors in the future. © 2014 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of The British Pharmacological Society.

Do women earn less even as social entrepreneurs? The gender pay gap among UK social enterprise directors

Based upon unique survey data collected using respondent driven sampling methods, we investigate whether there is a gender pay gap among social entrepreneurs in the UK. We find that women as social entrepreneurs earn 29% less than their male colleagues, above the average UK gender pay gap of 19%. We estimate the adjusted pay gap to be about 23% after controlling for a range of demographic, human capital and job characteristics, as well as personal preferences and values. These differences are hard to explain by discrimination since these CEOs set their own pay. Income may not be the only aim in an entrepreneurial career, so we also look at job satisfaction to proxy for non-monetary returns. We find female social entrepreneurs to be more satisfied with their job as a CEO of a social enterprise than their male counterparts. This result holds even when we control for the salary generated through the social enterprise. Our results extend research in labour economics on the gender pay gap as well as entrepreneurship research on women’s entrepreneurship to the novel context of social enterprise. It provides the first evidence for a “contented female social entrepreneur” paradox.