Serving children: the impact of poverty on children's experiences of services

This study arose from the identification of a gap in knowledge and corresponding need for the development of a better contemporary understanding of children's experiences of poverty. Focusing on children aged 10 - 14 years, the study aimed to provide a perspective on the lives of children and young people affected by poverty in Scotland through comparing the experiences of children living in poverty with those more economically advantaged

Analysis of dropout learning regarded as ensemble learning

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, p, and then, the neglected inputs and hidden units are combined with the learned network to express the final output. We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.Comment: 9 pages, 8 figures, submitted to Conferenc

Rim Pathway-Mediated Alterations in the Fungal Cell Wall Influence Immune Recognition and Inflammation

ACKNOWLEDGMENTS We acknowledge Jennifer Lodge, Woei Lam, and Rajendra Upadhya for developing and sharing the chitin and chitosan MTBH assay. We thank Todd Brennan of Duke University for providing MyD88-deficient mice. We acknowledge Neil Gow for providing access to the Dionex HPAEC-PAD instrumentation. We also acknowledge Connie Nichols for critical reading of the manuscript. These experiments were supported by an NIH grant to J.A.A. and F.L.W., Jr. (R01 AI074677). C.M.L.W. was supported by a fellowship provided through the Army Research Office of the Department of Defense (no. W911NF-11-1-0136 f) (F.L.W., Jr.). J.W., L.W., and C.M. were supported by the Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377) and the MRC, Centre for Medical Mycology (MR/N006364/1). FUNDING INFORMATION MRC Centre for Medical MycologyMR/N006364/1 Carol A. Munro HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID) https://doi.org/10.13039/100000060R01 AI074677J. Andrew Alspaugh Wellcome https://doi.org/10.13039/100010269097377 Carol A. Munro DOD | United States Army | RDECOM | Army Research Office (ARO) https://doi.org/10.13039/100000183W911NF-11-1-0136 f Chrissy M. Leopold WagerPeer reviewedPublisher PD

Regional specialization within the human striatum for diverse psychological functions

Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. Despite continuous efforts to subdivide the human striatum based on anatomical and resting-state functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5,809 human imaging studies. We (i) identified five distinct striatal zones that exhibited discrete patterns of coactivation with cortical brain regions across distinct psychological processes and (ii) identified the different psychological processes associated with each zone. We found that the reported pattern of cortical activation reliably predicted which striatal zone was most strongly activated. Critically, activation in each functional zone could be associated with distinct psychological processes directly, rather than inferred indirectly from psychological functions attributed to associated cortices. Consistent with well-established findings, we found an association of the ventral striatum (VS) with reward processing. Confirming less well-established findings, the VS and adjacent anterior caudate were associated with evaluating the value of rewards and actions, respectively. Furthermore, our results confirmed a sometimes overlooked specialization of the posterior caudate nucleus for executive functions, often considered the exclusive domain of frontoparietal cortical circuits. Our findings provide a precise functional map of regional specialization within the human striatum, both in terms of the differential cortical regions and psychological functions associated with each striatal zone

Taylor dispersion of gyrotactic swimming micro-organisms in a linear flow

The theory of generalized Taylor dispersion for suspensions of Brownian particles is developed to study the dispersion of gyrotactic swimming micro-organisms in a linear shear flow. Such creatures are bottom-heavy and experience a gravitational torque which acts to right them when they are tipped away from the vertical. They also suffer a net viscous torque in the presence of a local vorticity field. The orientation of the cells is intrinsically random but the balance of the two torques results in a bias toward a preferred swimming direction. The micro-organisms are sufficiently large that Brownian motion is negligible but their random swimming across streamlines results in a mean velocity together with diffusion. As an example, we consider the case of vertical shear flow and calculate the diffusion coefficients for a suspension of the alga <i>Chlamydomonas nivalis</i>. This rational derivation is compared with earlier approximations for the diffusivity

Multiple publications: The main reason for the retraction of papers in computer science

This paper intends to review the reasons for the retraction over the last decade. The paper particularly aims at reviewing these reasons with reference to computer science field to assist authors in comprehending the style of writing. To do that, a total of thirty-six retracted papers found on the Web of Science within Jan 2007 through July 2017 are explored. Given the retraction notices which are based on ten common reasons, this paper classifies the two main categories, namely random and nonrandom retraction. Retraction due to the duplication of publications scored the highest proportion of all other reasons reviewed

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Interdiffusion: A probe of vacancy diffusion in III-V materials

Copyright 1997 by the American Physical Society. Article is available at