A new approach to the analysis of short-range order in alloys using total scattering

In spite of its influence on a number of physical properties, short-range order in crystalline alloys has received little recent attention, largely due to the complexity of the experimental methods involved. In this work, a novel approach that could be used for the analysis of ordering transitions and short-range order in crystalline alloys using total scattering and reverse Monte Carlo (RMC) refinements is presented. Calculated pair distribution functions representative of different types of short-range order are used to illustrate the level of information contained within these experimentally accessible functions and the insight into ordering which may be obtained using this new method. Key considerations in the acquisition of data of sufficient quality for successful analysis are also discussed. It is shown that the atomistic models obtained from RMC refinements may be analysed to identify directly the Clapp configurations that are present. It is further shown how these configurations can be enhanced compared with a random structure, and how their degradation pathways and the distribution of Warren-Cowley parameters, can then be used to obtain a detailed, quantitative structural description of the short-range order occurring in crystalline alloys.Science and Technology Facilities CouncilThis is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.actamat.2016.05.03

The Human Fungal Pathogen Cryptococcus neoformans Escapes Macrophages by a Phagosome Emptying Mechanism That Is Inhibited by Arp2/3 Complex-Mediated Actin Polymerisation

The lysis of infected cells by disease-causing microorganisms is an efficient but risky strategy for disseminated infection, as it exposes the pathogen to the full repertoire of the host's immune system. Cryptococcus neoformans is a widespread fungal pathogen that causes a fatal meningitis in HIV and other immunocompromised patients. Following intracellular growth, cryptococci are able to escape their host cells by a non-lytic expulsive mechanism that may contribute to the invasion of the central nervous system. Non-lytic escape is also exhibited by some bacterial pathogens and is likely to facilitate long-term avoidance of the host immune system during latency. Here we show that phagosomes containing intracellular cryptococci undergo repeated cycles of actin polymerisation. These actin ‘flashes’ occur in both murine and human macrophages and are dependent on classical WASP-Arp2/3 complex mediated actin filament nucleation. Three dimensional confocal imaging time lapse revealed that such flashes are highly dynamic actin cages that form around the phagosome. Using fluorescent dextran as a phagosome membrane integrity probe, we find that the non-lytic expulsion of Cryptococcus occurs through fusion of the phagosome and plasma membranes and that, prior to expulsion, 95% of phagosomes become permeabilised, an event that is immediately followed by an actin flash. By using pharmacological agents to modulate both actin dynamics and upstream signalling events, we show that flash occurrence is inversely related to cryptococcal expulsion, suggesting that flashes may act to temporarily inhibit expulsion from infected phagocytes. In conclusion, our data reveal the existence of a novel actin-dependent process on phagosomes containing cryptococci that acts as a potential block to expulsion of Cryptococcus and may have significant implications for the dissemination of, and CNS invasion by, this organism.\ud \u

Low rank approximation of multidimensional data

In the last decades, numerical simulation has experienced tremendous improvements driven by massive growth of computing power. Exascale computing has been achieved this year and will allow solving ever more complex problems. But such large systems produce colossal amounts of data which leads to its own difficulties. Moreover, many engineering problems such as multiphysics or optimisation and control, require far more power that any computer architecture could achieve within the current scientific computing paradigm. In this chapter, we propose to shift the paradigm in order to break the curse of dimensionality by introducing decomposition to reduced data. We present an extended review of data reduction techniques and intends to bridge between applied mathematics community and the computational mechanics one. The chapter is organized into two parts. In the first one bivariate separation is studied, including discussions on the equivalence of proper orthogonal decomposition (POD, continuous framework) and singular value decomposition (SVD, discrete matrices). Then, in the second part, a wide review of tensor formats and their approximation is proposed. Such work has already been provided in the literature but either on separate papers or into a pure applied mathematics framework. Here, we offer to the data enthusiast scientist a description of Canonical, Tucker, Hierarchical and Tensor train formats including their approximation algorithms. When it is possible, a careful analysis of the link between continuous and discrete methods will be performed.IV Research and Transfer Plan of the University of SevillaInstitut CarnotJunta de AndalucíaIDEX program of the University of Bordeau

GroSS: Group-Size Series Decomposition for Grouped Architecture Search

We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks. Group-size Series (GroSS) decomposition is a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to enable simultaneous training of differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS is able to train an entire grouped convolution architecture search-space concurrently. We demonstrate this through architecture searches with performance objectives on multiple datasets and networks. GroSS enables more effective and efficient search for grouped convolutional architectures.Comment: Accepted for publication at ECCV 202

Assessing the Factorial Validity of the Attitudes and Belief Scale 2-Abbreviated Version: A Call for the Development a Gold Standard Method of Measuring Rational and Irrational Beliefs

Rational Emotive Behaviour Therapy (REBT) does not possess a measure of rational and irrational beliefs that meets internationally recognised standards for acceptable psychometric properties. Without such a measure the theory/practice of REBT cannot be rigorously evaluated, thus undermining its scientific veracity. The current study investigates the validity and reliability of a recently developed measure of rational and irrational beliefs: the Attitudes and Belief Scale 2-Abbreviated Version (ABS-2-AV). University students from three countries completed the ABS-2-AV (N = 397). An alternative models framework using confirmatory factor analysis indicated that a theoretically consistent eight-factor model of the ABS-2-AV provided the best fit of the data. A number of post-hoc modifications were required in order to achieve acceptable model fit results, and these modifications revealed important methodological limitations with the ABS-2-AV. Results indicated that the validity of the ABS-2-AV was undermined due to items measuring both the psychological process of interest (rational and irrational beliefs) and the context in which these beliefs processes are presented. This is a serious methodological limitation of the ABS-2 and all questionnaires derived from it, including the ABS-2-AV. This methodological limitation resulted in the ABS-2-AV possessing poor internal reliability. These limitations are discussed in relation to the broader REBT literature and the impact such problems have on research and practice. A call is made for REBT researchers to come together to develop a “gold standard” method of assessing rational and irrational beliefs that meets international standard for psychometric excellence

Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach

Background: In this study, we quantified age-related changes in the time-course of face processing by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our approach does not rely on peak measurements and can provide a more sensitive measure of processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded discrimination task between two faces. The phase spectrum of these faces was manipulated parametrically to create pictures that ranged between pure noise (0% phase information) and the undistorted signal (100% phase information), with five intermediate steps. Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers. Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers

Relationships between students' conceptions of constructivist learning and their regulation and processing strategies

The present study investigated relationships between students' conceptions of constructivist learning on the one hand, and their regulation and processing strategies on the other hand. Students in a constructivist, problem-based learning curriculum were questioned about their conceptions of knowledge construction and self-regulated learning, as well as their beliefs regarding their own (in)ability to learn and motivation to learn. Two hypothesized models were tested within 98 psychology students, using a structural equation modelling approach: The first model implemented regulation and processing variables of the Inventory of Learning Styles [ILS, Vermunt (Learning styles and regulation of learning in higher education - towards process-oriented instruction in autonomous thinking, 1992)], the second model of the Motivated Strategies for Learning Questionnaire [MSLQ, Pintrich and de Groot (Journal of Educational Psychology, 82, 33-40, 1990)]. Results showed that structural relations exist between conceptions of constructivist learning and regulation and processing strategies. Furthermore, students who express doubt with regard to their own learning capacities are at risk for adopting an inadequate regulation strategy. A three-tiered structure of conceptual, controlling, and operational level appeared valid for the MSLQ variables, but not entirely for those of the ILS

Factor structure and factorial invariance of the strengths and difficulties questionnaire among children of prisoners and their parents

Parental imprisonment has been linked to a variety of adverse psychological outcomes for children and adolescents. The Strengths and Difficulties Questionnaire (SDQ) has been widely used to assess behavioural and emotional difficulties among 7-17 year olds in the general population and more recently has been utilised among samples of children of prisoners. Previous research has variously tested traditional one-, three- and five- factor solutions to the SDQ, and more recently one bifactor solution has been examined. Based on a sample of children of prisoners (N = 724) and their non-imprisoned parent or caregiver (N = 658), the aim of the present study was to simultaneously compare nine alternative factor structures, including previously tested models and alternative bifactor solutions. Tests of factorial invariance and composite reliability were also performed. The five-factor model was found to provide the best fit for the data. Tests of factorial invariance revealed that the five-factor model provided an equally acceptable, but not identical fit, among boys and girls. Composite reliability scores were low for the Conduct Problems and Peer Problems subscales. The utility of the SDQ in measuring psychological functioning in response to parental imprisonment is discussed

Ambivalence toward men: comparing sexism among Polish, South African and British university students

This study extends the literature on attitudes toward gender roles by exploring whether the nature of sexism (i.e., benevolence and hostility directed at men) differs among university students from two under-researched countries, Poland (n = 190) and South Africa (n = 188), in a comparison with students in the United Kingdom (n = 166). Based on empirical literature applying Ambivalent Sexism Theory, and in the light of the socio-political context, it was hypothesized that: (1) both hostile and benevolent attitudes toward men in Poland would be more liberal than in South Africa and more conservative than in the United Kingdom, and (2), women would exhibit more hostile but less benevolent attitudes than men in relatively more conservative South Africa. The Ambivalence to Men Inventory was used to measure the two types of sexist attitudes about men. Findings supported the first hypothesis for hostile attitudes and partially for benevolent attitudes. South African and Polish students were more benevolent and hostile to men than British students, and students from South Africa were more hostile than those from Poland. Moreover, as predicted, a significant country-by-gender interaction revealed that South African women had more hostile and less benevolent attitudes to men than South African men. No such gender gap was present in the case of hostile attitudes in Poland and benevolent attitudes in the United Kingdom. Findings are discussed in terms of Ambivalent Sexism Theory and the countries’ socio-cultural context

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic