Scalable Multiresolution Image Segmentation and Its Application in Video Object Extraction Algorithm

This paper presents a novel multiresolution image segmentation method based on the discrete wavelet transform and Markov Random Field (MRF) modelling. A major contribution of this work is to add spatial scalability to the segmentation algorithm producing the same segmentation pattern at different resolutions. This property makes it suitable for the scalable object-based wavelet coding. The correlation between different resolutions of pyramid is considered by a multiresolution analysis which is incorporated into the objective function of the MRF segmentation algorithm. Allowing for smoothness terms in the objective function at different resolutions improves border smoothness and creates visually more pleasing objects/regions, particularly at lower resolutions where downsampling distortions are more visible. Application of the spatial segmentation in video segmentation, compared to traditional image/video object extraction algorithms, produces more visually pleasing shape masks at different resolutions which is applicable for object-based video wavelet coding. Moreover it allows for larger motion, better noise tolerance and less computational complexity. In addition to spatial scalability, the proposed algorithm outperforms the standard image/video segmentation algorithms, in both objective and subjective tests

Area and individual differences in personal crime victimization incidence: The role of individual, lifestyle/routine activities and contextual predictors

This article examines how personal crime differences between areas and between individuals are predicted by area and population heterogeneity and their synergies. It draws on lifestyle/routine activities and social disorganization theories to model the number of personal victimization incidents over individuals including routine activities and area characteristics, respectively, as well as their (cross-cluster) interactions. The methodology employs multilevel or hierarchical negative binomial regression with extra binomial variation using data from the British Crime Survey and the UK Census. Personal crime rates differ substantially across areas, reflecting to a large degree the clustering of individuals with measured vulnerability factors in the same areas. Most factors suggested by theory and previous research are conducive to frequent personal victimization except the following new results. Pensioners living alone in densely populated areas face disproportionally high numbers of personal crimes. Frequent club and pub visits are associated with more personal crimes only for males and adults living with young children, respectively. Ethnic minority individuals experience fewer personal crimes than whites. The findings suggest integrating social disorganization and lifestyle theories and prioritizing resources to the most vulnerable, rather than all, residents of poor and densely populated areas to prevent personal crimes

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

The comparative effectiveness and efficiency of cognitive behaviour therapy and generic counselling in the treatment of depression: evidence from the 2(nd) UK National Audit of psychological therapies.

BACKGROUND: Cognitive Behaviour Therapy (CBT) is the front-line psychological intervention for step 3 within UK psychological therapy services. Counselling is recommended only when other interventions have failed and its effectiveness has been questioned. METHOD: A secondary data analysis was conducted of data collected from 33,243 patients across 103 Improving Access to Psychological Therapies (IAPT) services as part of the second round of the National Audit of Psychological Therapies (NAPT). Initial analysis considered levels of pre-post therapy effect sizes (ESs) and reliable improvement (RI) and reliable and clinically significant improvement (RCSI). Multilevel modelling was used to model predictors of outcome, namely patient pre-post change on PHQ-9 scores at last therapy session. RESULTS: Counselling received more referrals from patients experiencing moderate to severe depression than CBT. For patients scoring above the clinical cut-off on the PHQ-9 at intake, the pre-post ES (95% CI) for CBT was 1.59 (1.58, 1.62) with 46.6% making RCSI criteria and for counselling the pre-post ES was 1.55 (1.52, 1.59) with 44.3% of patients meeting RCSI criteria. Multilevel modelling revealed a significant site effect of 1.8%, while therapy type was not a predictor of outcome. A significant interaction was found between the number of sessions attended and therapy type, with patients attending fewer sessions on average for counselling [M = 7.5 (5.54) sessions and a median (IQR) of 6 (3-10)] than CBT [M = 8.9 (6.34) sessions and a median (IQR) of 7 (4-12)]. Only where patients had 18 or 20 sessions was CBT significantly more effective than counselling, with recovery rates (95% CIs) of 62.2% (57.1, 66.9) and 62.4% (56.5, 68.0) respectively, compared with 44.4% (32.7, 56.6) and 42.6% (30.0, 55.9) for counselling. Counselling was significantly more effective at two sessions with a recovery rate of 34.9% (31.9, 37.9) compared with 22.2% (20.5, 24.0) for CBT. CONCLUSIONS: Outcomes for counselling and CBT in the treatment of depression were comparable. Research efforts should focus on factors other than therapy type that may influence outcomes, namely the inherent variability between services, and adopt multilevel modelling as the given analytic approach in order to capture the naturally nested nature of the implementation and delivery of psychological therapies. It is of concern that half of all patients, regardless of type of intervention, did not show reliable improvement

Rewarding work : cross-national differences in benefits, volunteering during unemployment, well-being and mental health

Due to increasing labour market flexibilisation a growing number of people are likely to experience unemployment and, as a consequence, lower mental health and well-being. This article examines cross-national differences in well-being and mental health between unemployed people who engage in voluntary work and those who do not, using multilevel data from the European Quality of Life Survey on unemployed individuals in 29 European countries and other external sources. This article finds that, regardless of their voluntary activity, unemployed people have higher levels of well-being and mental health in countries with more generous unemployment benefits. Unexpectedly, the results also suggest that regular volunteering can actually be detrimental for mental health in countries with less generous unemployment benefits. This article concludes that individual agency exercised through voluntary work can partially improve well-being but the generosity of unemployment benefits is vital for alleviating the negative mental health effects of unemployment

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Clinicians' caseload management behaviours as explanatory factors in patients' length of time on caseloads : a predictive multilevel study in paediatric community occupational therapy

Peer reviewedPublisher PD