The Interplay Between Families and Schools: Immigrant and Native Differentials in Educational Outcomes

We examine the effects of school context on educational outlooks and outcomes of the children of immigrants, in comparison with natives in Spain, an under-represented case in the international literature and a fast growing immigration destination in Europe. Using two sources of hierarchical data, 2011 Chances Survey and the 2010 Secondary Schooling National Evaluation Survey, which cluster students across schools, we investigate the factors that contribute to the formation of long term educational careers. To start with we analyze performance from both an objective (test scores in mathematics) and subjective perspective (estimation by children and also their parents of whether individual school results will allow them to proceed to tertiary education). Then we turn our attention to the adjusted educational expectations (controlled for prior performance) of children. Our results reveal the different way that school context works for immigrant and native origin children. Our multilevel regression analysis finds significantly worse school results among immigrants (test scores). Although immigrant children themselves understand the constraints that such disadvantage imposes on their future educational careers, immigrant parents seem to hold on to a rather unrealistic position. This parental optimism in turn seems to boost the career expectation of immigrant children independent of school effects. Thus while school context determines the performance of immigrant origin students to a greater extent than those of natives, the opposite is true for expectations. The formation of aspirations is more family-oriented among immigrants, and thus more positive, than among natives

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Social network- and community-level influences on contraceptive use: evidence from rural Poland.

The diffusion of 'modern' contraceptives-as a proxy for the spread of low-fertility norms-has long interested researchers wishing to understand global fertility decline. A fundamental question is how local cultural norms and other people's behaviour influence the probability of contraceptive use, independent of women's socioeconomic and life-history characteristics. However, few studies have combined data at individual, social network and community levels to simultaneously capture multiple levels of influence. Fewer still have tested if the same predictors matter for different contraceptive types. Here, we use new data from 22 high-fertility communities in Poland to compare predictors of the use of (i) any contraceptives-a proxy for the decision to control fertility-with those of (ii) 'artificial' contraceptives-a subset of more culturally taboo methods. We find that the contraceptive behaviour of friends and family is more influential than are women's own characteristics and that community level characteristics additionally influence contraceptive use. Highly educated neighbours accelerate women's contraceptive use overall, but not their artificial method use. Highly religious neighbours slow women's artificial method use, but not their contraceptive use overall. Our results highlight different dimensions of sociocultural influence on contraceptive diffusion and suggest that these may be more influential than are individual characteristics. A comparative multilevel framework is needed to understand these dynamics

Lost in translation: Toward a formal model of multilevel, multiscale medicine

For a broad spectrum of low level cognitive regulatory and other biological phenomena, isolation from signal crosstalk between them requires more metabolic free energy than permitting correlation. This allows an evolutionary exaptation leading to dynamic global broadcasts of interacting physiological processes at multiple scales. The argument is similar to the well-studied exaptation of noise to trigger stochastic resonance amplification in physiological subsystems. Not only is the living state characterized by cognition at every scale and level of organization, but by multiple, shifting, tunable, cooperative larger scale broadcasts that link selected subsets of functional modules to address problems. This multilevel dynamical viewpoint has implications for initiatives in translational medicine that have followed the implosive collapse of pharmaceutical industry 'magic bullet' research. In short, failure to respond to the inherently multilevel, multiscale nature of human pathophysiology will doom translational medicine to a similar implosion

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018

The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing

We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is a powerful tool for integrating behavioural and neurophysiological results

Toward the next generation of research into small area effects on health : a synthesis of multilevel investigations published since July 1998.

To map out area effects on health research, this study had the following aims: (1) to inventory multilevel investigations of area effects on self rated health, cardiovascular diseases and risk factors, and mortality among adults; (2) to describe and critically discuss methodological approaches employed and results observed; and (3) to formulate selected recommendations for advancing the study of area effects on health. Overall, 86 studies were inventoried. Although several innovative methodological approaches and analytical designs were found, small areas are most often operationalised using administrative and statistical spatial units. Most studies used indicators of area socioeconomic status derived from censuses, and few provided information on the validity and reliability of measures of exposures. A consistent finding was that a significant portion of the variation in health is associated with area context independently of individual characteristics. Area effects on health, although significant in most studies, often depend on the health outcome studied, the measure of area exposure used, and the spatial scale at which associations are examined