Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Distance between home and work: A multilevel analysis of individual workers, neighbourhoods, and employment sites in Northern Ireland

Abstract. We explore the distances between home and work for employees at twenty-eight different employment sites across Northern Ireland. Substantively, this is important for better understanding the geography of labour catchments. Methodologically, with data on the distances between place of residence (566 wards) and place of work for some 15 000 workers, and the use of multilevel modelling (MLM), the analysis adds to the evidence derived from other census-based and survey-based studies. Descriptive analysis is supplemented with MLM that simultaneously explores individual, neighbourhood, and site variations in travel-to-work patterns using hierarchical and cross-classified model specifications, including individual and ecological predictor variables (and their cross-level interactions). In doing so we apportion variability to different levels and spatial contexts, and also outline the factors that shape spatial mobility. We find, as expected, that factors such as gender and occupation influence the distance between home and work, and also confirm the importance of neighbourhood characteristics (such as population density observed in ecological analyses at ward level) in shaping individual outcomes, with major differences found between urban and rural locations. Beyond this, the analysis of variability also points to the relative significance of residential location, with less individual variability in travel-to-work distance between workers within wards than within employment sites. We conclude by suggesting that, whilst some general ‘rules’ about the factors that shape labour catchments are possible (eg workers in rural areas and in higher occupations travel further than others), the complex variability between places highlighted by the MLM analysis illustrates the salience of place-specific uniqueness.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Age and gender differences in health-related quality of life of children and adolescents in Europe: a multilevel analysis

OBJECTIVES: To determine age and gender differences in health-related quality of life (HRQOL) in children and adolescents across 12 European countries using a newly developed HRQOL measure (KIDSCREEN). METHODS: The KIDSCREEN-52 questionnaire was filled in by 21,590 children and adolescents aged 8-18 from 12 countries. We used multilevel regression analyses to model the hierarchical structure of the data. In addition, effect sizes were computed to test for gender differences within each age group. RESULTS: Children generally showed better HRQOL than adolescents (P < 0.001). While boys and girls had similar HRQOL at young age, girls' HRQOL declined more than boys' (P < 0.001) with increasing age, depending on the HRQOL scale. There was significant variation between countries both at the youngest age and for age trajectories. CONCLUSIONS: For the first time, gender and age differences in children's and adolescents' HRQOL across Europe were assessed using a comprehensive and standardised instrument. Gender and age differences exist for most HRQOL scales. Differences in HRQOL across Europe point to the importance of national contexts for youth's well-being.

Determinants of sponsorship fit:a multilevel analysis

Sponsorship fit is frequently mentioned and empirically examined as a success factor of sponsorship. While sponsorship fit has been considered as a determinant of sponsorship success, little knowledge exists about the antecedents of sponsorship fit. In the present paper, individual and firm-level antecedents of sponsorship fit are examined in a single hierarchical linear model. Results show that sponsorship fit is influenced by the perception of benefits, the firm’s regional identification, sincerity, relatedness to the sponsored activity, and its dominance. On a partnership level, results show that contract length contributes to sponsorship fit while contract value is found to be unrelated.

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method.

Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand’s national and regional groundwater monitoring programs

Baseline monitoring of groundwater quality aims to characterize the ambient condition of the resource and identify spatial or temporal trends. Sites comprising any baseline monitoring network must be selected to provide a representative perspective of groundwater quality across the aquifer(s) of interest. Hierarchical cluster analysis (HCA) has been used as a means of assessing the representativeness of a groundwater quality monitoring network, using example datasets from New Zealand. HCA allows New Zealand's national and regional monitoring networks to be compared in terms of the number of water-quality categories identified in each network, the hydrochemistry at the centroids of these water-quality categories, the proportions of monitoring sites assigned to each water-quality category, and the range of concentrations for each analyte within each water-quality category. Through the HCA approach, the National Groundwater Monitoring Programme (117 sites) is shown to provide a highly representative perspective of groundwater quality across New Zealand, relative to the amalgamated regional monitoring networks operated by 15 different regional authorities (680 sites have sufficient data for inclusion in HCA). This methodology can be applied to evaluate the representativeness of any subset of monitoring sites taken from a larger network.

Geographic remoteness, area-level socioeconomic disadvantage and inequalities in colorectal cancer survival in Queensland : a multilevel analysis

Background To explore the impact of geographical remoteness and area-level socioeconomic disadvantage on colorectal cancer (CRC) survival. Methods Multilevel logistic regression and Markov chain Monte Carlo simulations were used to analyze geographical variations in five-year all-cause and CRC-specific survival across 478 regions in Queensland Australia for 22,727 CRC cases aged 20–84 years diagnosed from 1997–2007. Results Area-level disadvantage and geographic remoteness were independently associated with CRC survival. After full multivariate adjustment (both levels), patients from remote (odds Ratio [OR]: 1.24, 95%CrI: 1.07-1.42) and more disadvantaged quintiles (OR = 1.12, 1.15, 1.20, 1.23 for Quintiles 4, 3, 2 and 1 respectively) had lower CRC-specific survival than major cities and least disadvantaged areas. Similar associations were found for all-cause survival. Area disadvantage accounted for a substantial amount of the all-cause variation between areas. Conclusions We have demonstrated that the area-level inequalities in survival of colorectal cancer patients cannot be explained by the measured individual-level characteristics of the patients or their cancer and remain after adjusting for cancer stage. Further research is urgently needed to clarify the factors that underlie the survival differences, including the importance of geographical differences in clinical management of CRC.

Hierarchical multilevel arrays of self-assembled gold nanoparticles : Control of resistivity-temperature dependence

The possibility to control the electric resistivity-temperature dependence of the nanosized resistive components made using hierarchical multilevel arrays of self-assembled gold nanoparticles prepared by multiple deposition/annealing is demonstrated. It is experimentally shown that the hierarchical three-level patterns, where the nanoparticles of sizes ranging from several nanometers to several tens of nanometer play a competitive roles in the electric conductivity, demonstrate sharp changes in the activation energy. These patterns can be used for the precise tuning of the resistivity-temperature behavior of nanoelectronic components.

Relationships between Religion and Two Forms of Homonegativity in Europe—A Multilevel Analysis of Effects of Believing, Belonging and Religious Practice

This paper examines relationships between religion and two forms of homonegativity
across 43 European countries using a bivariate response binary logistic multilevel model. The model analyzes effects of religious believing, belonging and practice on two response variables: a) a moral rejection of homosexuality as a practice and b) intolerance toward homosexuals as a group. The findings indicate that both forms of homonegativity are prevalent in Europe. Traditional doctrinal religious believing (belief in a personal God) is positively related to a moral rejection of homosexuality but to a much lesser extent associated with intolerance toward homosexuals as a group. Members of religious denominations are more likely than non-members to reject homosexuality as morally wrong and to reject homosexuals as neighbors. The analysis found significant differences between denominations that are likely context-dependent. Attendance at religious services is positively related to homonegativity in a majority of countries. The findings vary considerably across countries: Religion is more strongly related to homonegativity in Western than in Eastern Europe. In the post-soviet countries homonegativity appears to be largely a secular phenomenon. National contexts of high religiosity, high perceived government corruption, high income inequality and shortcomings in the implementation of gay rights in the countries’ legislations are statistically related to higher levels of both moralistic homonegativity and intolerance toward homosexuals as a group.

Identification and ethnic diversity underlie support for multicultural rights : a multilevel analysis in Bulgaria

Bulgaria is historically a multicultural society, composed of the Bulgarian (ethnic) majority and a number of ethnic minorities among which Bulgarian Turks and Roma are the largest. Both minority communities are stigmatized in contemporary Bulgaria, though to different degrees and for different reasons. Ethnic minorities' rights to preserve their culture, customs, and language are a topic of contentious debate. The purpose of this study was to examine individual- and context-level antecedents of the ethnic Bulgarian majority's support for multicultural rights of ethnic minorities. Multilevel regression analyses were conducted with International Social Survey Programme ISSP 2003 data (N = 920 in 28 Bulgarian districts). At the individual-level, an ethnic conception of the nation and anti-Roma symbolic prejudice were negatively related to support for multicultural rights, whereas national identification was positively related to the support of these rights. Over and above individual-level effects, and in line with recent extensions of intergroup contact theory, thepercentage ofBulgarianTurks withindistricts was positively related to support for multicultural rights. Importantly, support for multicultural rights was particularly high in districts characterized by ethnic diversity, that is, in districts with high proportions of both Bulgarian Turks and Roma. The beneficial effects of ethnic diversity and theoretical implications of findings are discussed.

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities.

Study Design. Reliability study. Objectives. To assess between-acquisition reliability of new multilevel trunk cross sections measurements, in order to define what is a real change when comparing 2 trunk surface acquisitions of a same patient, before and after surgery or throughout the clinical monitoring. Summary of Background Data. Several cross-sectional surface measurements have been proposed in the literature for noninvasive assessment of trunk deformity in patients with adolescent idiopathic scoliosis (AIS). However, only the maximum values along the trunk are evaluated and used for monitoring progression and assessing treatment outcome. Methods. Back surface rotation (BSR), trunk rotation (TR), and coronal and sagittal trunk deviation are computed on 300 cross sections of the trunk. Each set of 300 measures is represented as a single functional data, using a set of basis functions. To evaluate between-acquisition variability at all trunk levels, a test-retest reliability study is conducted on 35 patients with AIS. A functional correlation analysis is also carried out to evaluate any redundancy between the measurements. Results. Each set of 300 measures was successfully described using only 10 basis functions. The test-retest reliability of the functional measurements is good to very good all over the trunk, except above the shoulders level. The typical errors of measurement are between 1.20° and 2.2° for the rotational measures and between 2 and 6 mm for deviation measures. There is a very strong correlation between BSR and TR all over the trunk, a moderate correlation between coronal trunk deviation and both BSR and TR, and no correlation between sagittal trunk deviation and any other measurement. Conclusion. This novel representation of trunk surface measurements allows for a global assessment of trunk surface deformity. Multilevel trunk measurements provide a broader perspective of the trunk deformity and allow a reliable multilevel monitoring during clinical follow-up of patients with AIS and a reliable assessment of the esthetic outcome after surgery.

Global trends and factors associated with the illegal killing of elephants: a hierarchical bayesian analysis of carcass encounter data

Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE), set up by the 10th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002–2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a Bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE) as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.