Ethnic minority immigrants and their children in Britain

According to the 2001 UK Census ethnic minority groups account for 4.6 million or 7.9 percent of the total UK population. The 2001 British Labour Force Survey indicates that the descendants of Britain’s ethnic minority immigrants form an important part of the British population (2.8 percent) and of the labour force (2.1 percent). In this paper, we use data from the British Labour Force Survey over the period 1979-2005 to investigate educational attainment and economic behaviour of ethnic minority immigrants and their children in Britain. We compare different ethnic minority groups born in Britain to their parent’s generation and to equivalent groups of white native born individuals. Intergenerational comparisons suggest that British born ethnic minorities are on average more educated than their parents as well more educated than their white native born peers. Despite their strong educational achievements, we find that ethnic minority immigrants and their British born children exhibit lower employment probabilities than their white native born peers. However, significant differences exist across immigrant/ethnic groups and genders. British born ethnic minorities appear to have slightly higher wages than their white native born peers. But if British born ethnic minorities were to face the white native regional distribution and were attributed white native characteristics, their wages would be considerably lower. The substantial employment gap between British born ethnic minorities and white natives cannot be explained by observable differences. We suggest some possible explanations for these gaps

Coarse Bifurcation Studies of Bubble Flow Microscopic Simulations

The parametric behavior of regular periodic arrays of rising bubbles is investigated with the aid of 2-dimensional BGK Lattice-Boltzmann (LB) simulators. The Recursive Projection Method is implemented and coupled to the LB simulators, accelerating their convergence towards what we term coarse steady states. Efficient stability/bifurcation analysis is performed by computing the leading eigenvalues/eigenvectors of the coarse time stepper. Our approach constitutes the basis for system-level analysis of processes modeled through microscopic simulations.Comment: 4 pages, 3 figure

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordModel predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.The authors would like to acknowledge the financial support of the EC FP6 Project: CONNECT [COOP-2006-31638] and the EC FP7 project CAFE [KBBE-212754]

Computational modelling of social cognition and behaviour

Philosophers have always been interested in asking moral questions, but social scientists have generally been more occupied with asking questions about morality. How do people differ with regards to their morality? How frequently are moral values inconsistent, thus resulting in internal conflicts? How likely are people to revise their moral beliefs? The aim of these questions is to explore moral reasoning and identify patterns of moral behaviour between people. Simultaneously, social scientists have moved beyond the exploration of small-scale, static snapshot of networks onto nuanced, data-driven analyses of the structure, content, and dynamics of large-scale social processes. This drives researchers to use far more elaborate tools, such as automated text analysis, online field experiments, mass collaboration, machine learning, and more generally computational modelling, to formulate and test theories (e.g., Evans & Aceves, 2016; Molina & Garip, 2019; Nelson, 2020; Salganik, 2019). It is fair to argue that social sciences are on the verge of a new era, an era in which computational methods and large-scale data are the primary tools/sources of gaining information and knowledge. In this dissertation, I will focus on developing formal models of the cognitive dissonance involved in moral values conflicts within individuals, and how this might be reduced. I will also attempt to extend this to connect with research linking moral and political psychology. Then I will try to explain echo chamber development, as a socio-cognitive phenomenon, arising from dynamics described in chapters 2 and 3. Finally, I will focus on moral belief updating, as an alternate (class of) response(s) in chapter 6. I try to explain these phenomena by bringing together cognitive and social theories. The three principal theories we build upon are Festinger’s Cognitive Dissonance, Bandura’s Moral Disengagement and Haidt’s Moral Fountations Theory. As it is detailed in the forthcoming paragraphs, the union of these theories, alongside with computational modelling, sparks off some interesting hypotheses. We now go ahead and discuss why computational modelling is a powerful tool in social sciences, and then present a historical background for each of the aforementioned theories

Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning

Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token-level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al.,2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.Comment: To be presented at CoNLL 202

A Method for Prostate and Breast Cancer Cell Spheroid Cultures Using Gelatin Methacryloyl-Based Hydrogels.

Modern tissue engineering technologies have delivered tools to recreate a cell's naturally occurring niche in vitro and to investigate normal and pathological cell-cell and cell-niche interactions. Hydrogel biomaterials mimic crucial properties of native extracellular matrices, including mechanical support, cell adhesion sites and proteolytic degradability. As such, they are applied as 3D cell culture platforms to replicate tissue-like architectures observed in vivo, allowing physiologically relevant cell behaviors. Here we review bioengineered 3D approaches used for prostate and breast cancer. Furthermore, we describe the synthesis and use of gelatin methacryloyl-based hydrogels as in vitro 3D cancer model. This platform is used to engineer the microenvironments for prostate and breast cancer cells to study processes regulating spheroid formation, cell functions and responses to therapeutic compounds. Collectively, these bioengineered 3D approaches provide cell biologists with innovative pre-clinical tools that integrate the complexity of the disease seen in patients to advance our knowledge of cancer cell physiology and the contribution of a tumor's surrounding milieu

Fully Automatic 3D-TEE Segmentation for the Planning of Transcatheter Aortic Valve Implantation

A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.This work was supported by the project "ON.2 SR&TD Integrated Program (Norte-07-0124-FEDER-000017)" cofunded by the Programa Operacional Regional do Norte (ON.2- O Novo Norte), Quadro de Referencia Estrategico Nacional, through Fundo Europeu de Desenvolvimento Regional. The work of S. Queiros and P. Morais was supported by the FCT-Fundacao para a Ciencia e a Tecnologia and the European Social Found through the Programa Operacional Capital Humano in the scope of the Ph.D. Grants SFRH/BD/93443/2013 and SFRH/BD/95438/2013, respectively. J. L. Vilaca and J. D'hooge are joint last authors. Asterisk indicates corresponding author.info:eu-repo/semantics/publishedVersio