Flutter of buckled, simply supported, rectangular panels at supersonic speeds

Flutter analysis on buckled supported rectangular panels under supersonic surface flo

Words, rules, and mechanisms of language acquisition

We review recent artificial language learning studies, especially those following Endress and Bonatti (2007), suggesting that humans can deploy a variety of learning mechanisms to acquire artificial languages. Several experiments provide evidence for multiple learning mechanisms that can be deployed in fluent speech: one mechanism encodes the positions of syllables within words and can be used to extract generalization, while the other registers co-occurrence statistics of syllables and can be used to break a continuum into its components. We review dissociations between these mechanisms and their potential role in language acquisition. We then turn to recent criticisms of the multiple mechanisms hypothesis and show that they are inconsistent with the available data. Our results suggest that artificial and natural language learning is best understood by dissecting the underlying specialized learning abilities, and that these data provide a rare opportunity to link important language phenomena to basic psychological mechanisms

Using a cognitive architecture to examine what develops

Different theories of development propose alternative mechanisms by which development occurs. Cognitive architectures can be used to examine the influence of each proposed mechanism of development while keeping all other mechanisms constant. An ACT-R computational model that matched adult behavior in solving a 21-block pyramid puzzle was created. The model was modified in three ways that corresponded to mechanisms of development proposed by developmental theories. The results showed that all the modifications (two of capacity and one of strategy choice) could approximate the behavior of 7-year-old children on the task. The strategy-choice modification provided the closest match on the two central measures of task behavior (time taken per layer, r = .99, and construction attempts per layer, r = .73). Modifying cognitive architectures is a fruitful way to compare and test potential developmental mechanisms, and can therefore help in specifying “what develops.

Rates of referable eye disease in the Scottish National Diabetic Retinopathy Screening Programme

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.Peer reviewedPublisher PD

Continuous, Semi-discrete, and Fully Discretized Navier-Stokes Equations

The Navier--Stokes equations are commonly used to model and to simulate flow phenomena. We introduce the basic equations and discuss the standard methods for the spatial and temporal discretization. We analyse the semi-discrete equations -- a semi-explicit nonlinear DAE -- in terms of the strangeness index and quantify the numerical difficulties in the fully discrete schemes, that are induced by the strangeness of the system. By analyzing the Kronecker index of the difference-algebraic equations, that represent commonly and successfully used time stepping schemes for the Navier--Stokes equations, we show that those time-integration schemes factually remove the strangeness. The theoretical considerations are backed and illustrated by numerical examples.Comment: 28 pages, 2 figure, code available under DOI: 10.5281/zenodo.998909, https://doi.org/10.5281/zenodo.99890

The Economic Resource Receipt of New Mothers

U.S. federal policies do not provide a universal social safety net of economic support for women during pregnancy or the immediate postpartum period but assume that employment and/or marriage will protect families from poverty. Yet even mothers with considerable human and marital capital may experience disruptions in employment, earnings, and family socioeconomic status postbirth. We use the National Survey of Families and Households to examine the economic resources that mothers with children ages 2 and younger receive postbirth, including employment, spouses, extended family and social network support, and public assistance. Results show that many new mothers receive resources postbirth. Marriage or postbirth employment does not protect new mothers and their families from poverty, but education, race, and the receipt of economic supports from social networks do

Effects of perceived cocaine availability on subjective and objective responses to the drug

<p>Abstract</p> <p>Rationale</p> <p>Several lines of evidence suggest that cocaine expectancy and craving are two related phenomena. The present study assessed this potential link by contrasting reactions to varying degrees of the drug's perceived availability.</p> <p>Method</p> <p>Non-treatment seeking individuals with cocaine dependence were administered an intravenous bolus of cocaine (0.2 mg/kg) under 100% ('unblinded'; N = 33) and 33% ('blinded'; N = 12) probability conditions for the delivery of drug. Subjective ratings of craving, high, rush and low along with heart rate and blood pressure measurements were collected at baseline and every minute for 20 minutes following the infusions.</p> <p>Results</p> <p>Compared to the 'blinded' subjects, their 'unblinded' counterparts had similar craving scores on a multidimensional assessment several hours before the infusion, but reported higher craving levels on a more proximal evaluation, immediately prior to the receipt of cocaine. Furthermore, the 'unblinded' subjects displayed a more rapid onset of high and rush cocaine responses along with significantly higher cocaine-induced heart rate elevations.</p> <p>Conclusion</p> <p>These results support the hypothesis that cocaine expectancy modulates subjective and objective responses to the drug. Provided the important public health policy implications of heavy cocaine use, health policy makers and clinicians alike may favor cocaine craving assessments performed in the settings with access to the drug rather than in more neutral environments as a more meaningful marker of disease staging and assignment to the proper level of care.</p

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics