SOR models and Ethnicity data in LIS and LES : country by country report

This research considers the idea that a single metric expressing distance between social groups may be an adequate tool for investigating the relationship between ethnic/nationality minority group membership and social stratification. A Stereotyped Ordered Regression (SOR) model is proposed as a methodology for deriving this metric 1, and this paper considers the role of SOR models for the variety of countries with appropriate data made available by the Luxembourg Income and Employment studies (LIS and LES). In particular, by making the referents of this metric relatively consistent between different countries, it is suggested that a cross-nationally comparable representation of ethnic/nationality group membership can be derived which reduces the difficulties of international comparative research on ethnicity. Section one of this paper deals with three introductory issues: the clarification of the proposed methodology; the possibilities for ethnicity analyses as available from the LIS/LES datasets; and the theoretical framework used to draw substantive cross-national comparisons. Section two comprises a summary of the descriptive patterns observed for selected indicators of social stratification by ethnic/nationality groups for each country, and the presentation of the SOR orderings derived from them. In section three, the possibilities for using those SOR orderings in analytical human capital style models of social stratification are considered. Lastly in section four some of the more prominent conclusions are drawn together.

SOR Models and Ethnicity Data in LIS and LES: Country by Country Report

This research considers the idea that a single metric expressing distance between social groups may be an adequate tool for investigating the relationship between ethnic/nationality minority group membership and social stratification. A Stereotyped Ordered Regression (SOR) model is proposed as a methodology for deriving this metric, and this paper considers the role of SOR models for the variety of countries with appropriate data made available by the Luxembourg Income and Employment studies (LIS and LES). In particular, by making the referents of this metric relatively consistent between different countries, it is suggested that a cross-nationally comparable representation of ethnic/nationality group membership can be derived which reduces the difficulties of international comparative research on ethnicity. Section one of this paper deals with three introductory issues: the clarification of the proposed methodology; the possibilities for ethnicity analyses as available from the LIS/LES datasets; and the theoretical framework used to draw substantive cross-national comparisons. Section two comprises a summary of the descriptive patterns observed for selected indicators of social stratification by ethnic / nationality groups for each country, and the presentation of the SOR orderings derived from them. In section three, the possibilities for using those SOR orderings in analytical human capital style models of social stratification are considered. Lastly in section four some of the more prominent conclusions are drawn together

SOR models and ethnicity data in LIS and LES : a country by country report.

This research considers the idea that a single metric expressing distance between social groups may be an adequate tool for investigating the relationship between ethnic / nationality minority group membership and social stratification. A Stereotyped Ordered Regression (SOR) model is proposed as a methodology for deriving this metric1, and this paper considers the role of SOR models for the variety of countries with appropriate data made available by the Luxembourg Income and Employment studies (LIS and LES). In particular, by making the referents of this metric relatively consistent between different countries, it is suggested that a cross-nationally comparable representation of ethnic / nationality group membership can be derived which reduces the difficulties of international comparative research on ethnicity. Section one of this paper deals with three introductory issues : the clarification of the proposed methodology; the possibilities for ethnicity analyses as available from the LIS / LES datasets; and the theoretical framework used to draw substantive cross-national comparisons. Section two comprises a summary of the descriptive patterns observed for selected indicators of social stratification by ethnic / nationality groups for each country, and the presentation of the SOR orderings derived from them. In section three, the possibilities for using those SOR orderings in analytical human capital style models of social stratification are considered. Lastly in section four some of the more prominent conclusions are drawn together

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds. Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smart-city mobility framework providing a position every second of two public transportation bus tracks. Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves. Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2\u2009=\u200959.7% SOR2\u2009=\u200953%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset\u2019s point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values

Dissociations in the effect of delay on object recognition: evidence for an associative model of recognition memory

Rats were administered 3 versions of an object recognition task: In the spontaneous object recognition task (SOR) animals discriminated between a familiar object and a novel object; in the temporal order task they discriminated between 2 familiar objects, 1 of which had been presented more recently than the other; and, in the object-in-place task, they discriminated among 4 previously presented objects, 2 of which were presented in the same locations as in preexposure and 2 in different but familiar locations. In each task animals were tested at 2 delays (5 min and 2 hr) between the sample and test phases in the SOR and object-in-place task, and between the 2 sample phases in the temporal order task. Performance in the SOR was poorer with the longer delay, whereas in the temporal order task performance improved with delay. There was no effect of delay on object-in-place performance. In addition the performance of animals with neurotoxic lesions of the dorsal hippocampus was selectively impaired in the object-in-place task at the longer delay. These findings are interpreted within the framework of Wagner’s (1981) model of memory

Computer Aided Aroma Design. II. Quantitative structure-odour relationship

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and their odour quality are definitely subjective or their odour intensity are partly subjective as stated in Rossitier’s review (1996). The CAAD methodology and a novel molecular framework were presented in part I. Part II focuses on a classification methodology to characterize the odour quality of molecules based on Structure – Odour Relation (SOR). Using 2D and 3D molecular descriptors, Linear Discriminant Analysis (LDA) and Artificial Neural Network are compared in favour of LDA. The classification into balsamic / non balsamic quality was satisfactorily solved. The classification among five sub notes of the balsamic quality was less successful, partly due to the selection of the Aldrich’s Catalog as the reference classification. For the second case, it is shown that the sweet sub note considered in Aldrich’s Catalog is not a relevant sub note, confirming the alternative and popular classification of Jaubert et al., (1995), the field of odours

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.Comment: 19 pages, 4 figure

The Delphi and GRADE methodology used in the PSOGI 2018 consensus statement on Pseudomyxoma Peritonei and Peritoneal Mesothelioma

Pseudomyxoma Peritonei (PMP) and Peritoneal Mesothelioma (PM) are both rare peritoneal malignancies. Currently, affected patients may be treated with Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy offering long-term survival or even cure in selected patients. However, many issues regarding the optimal treatment strategy are currently under debate. To aid physicians involved in the treatment of these patients in clinical decision making, the PSOGI executive committee proposed to create a consensus statement on PMP and PM. This manuscript describes the methodology of the consensus process. The Delphi technique is a reliable method for attaining consensus on a topic that lacks scientific evidence through multiple voting rounds which feeds back responses to the participants in between rounds. The GRADE system provides a structured framework for presenting and grading the available evidence. Separate questionnaires were created for PMP and PM and sent during two voting rounds to 80 and 38 experts, respectively. A consensus threshold of 51.0% was chosen. After the second round, consensus was reached on 92.9%–100.0% of the questions. The results were presented and discussed in the plenary session at the PSOGI 2018 international meeting in Paris. A third round for the remaining issues is currently in progress. In conclusion, using the Delphi technique and GRADE methodology, consensus was reached in many issues regarding the treatment of PM and PMP amongst an international panel of experts. The main results will be published in the near future