1,808 research outputs found
Changes in hemodynamic response to faces, scenes, and objects in a visual statistical learning task: An fMRI analysis
Learning causes changes in brain activity and neural connections. Statistical learning is an implicit learning process that involves extracting regularities from the environment and finding patterns in stimuli based on their transitional probabilities. The following study describes an attempt to elucidate temporal changes in hemodynamic activity for three category-specific brain areas using functional magnetic resonance imaging (fMRI). Blood oxygen-level dependent signal (BOLD) responses were collected while subjects viewed faces, scenes, and objects with high and low transitional probabilities in an fMRI scanner. We expected brain activity to show a temporal shift in timing of activation when comparing BOLD signal responses before and after visual statistical learning. Instead, a general, yet insignificant, trend in the magnitude of activation was found. Although these findings suggest category-specific brain areas may undergo magnitude changes in activation for item-specific stimuli in response to visual statistical learning, further confirmatory analyses and comparisons to behavioral data are needed
Infant Language Assessment Predicts Later Math Disabilities
Prevention of cognitive disabilities currently remains out of reach. Yet, interventions are crucial to maximizing developmental outcomes later in life. To be effective, interventions must occur at the earliest age possible to mitigate potential developmental problems. This study is an attempt to identify newborn infants at risk for developing math disabilities later in life. Several studies used assessment tests at relatively late ages in order to predict future cognitive abilities (Aarnoudse-Moens et al., 2013; Kiechl-Kohlendorfer et al., 2013). More recent research used MRI scans of neonate brains to investigate the relationships between academic abilities and preterm births (Ullman et al., 2015). While these studies laid groundwork for prediction models, they primarily focused on physiological and social factors associated with preterm births. The present research examined possible precursors of math disabilities utilizing event-related potential (ERP) brain wave responses recorded from infants within 36 hours after birth. We hypothesized that infant brain responses to speech and nonspeech stimuli could predict individuals predisposed to developing math difficulties later in life
Wireless Communication in Process Control Loop: Requirements Analysis, Industry Practices and Experimental Evaluation
Wireless communication is already used in process automation for process monitoring. The next stage of implementation of wireless technology in industrial applications is for process control. The need for wireless networked control systems has evolved because of the necessity for extensibility, mobility, modularity, fast deployment, and reduced installation and maintenance cost. These benefits are only applicable given that the wireless network of choice can meet the strict requirements of process control applications, such as latency. In this regard, this paper is an effort towards identifying current industry practices related to implementing process control over a wireless link and evaluates the suitability of ISA100.11a network for use in process control through experiments
Complex Line Bundles over Simplicial Complexes and their Applications
Discrete vector bundles are important in Physics and recently found
remarkable applications in Computer Graphics. This article approaches discrete
bundles from the viewpoint of Discrete Differential Geometry, including a
complete classification of discrete vector bundles over finite simplicial
complexes. In particular, we obtain a discrete analogue of a theorem of Andr\'e
Weil on the classification of hermitian line bundles. Moreover, we associate to
each discrete hermitian line bundle with curvature a unique piecewise-smooth
hermitian line bundle of piecewise constant curvature. This is then used to
define a discrete Dirichlet energy which generalizes the well-known cotangent
Laplace operator to discrete hermitian line bundles over Euclidean simplicial
manifolds of arbitrary dimension
Molecular circumscription of new species of Gyrocotyle Diesing, 1850 (Cestoda) from deep-sea chimaeriform holocephalans in the North Atlantic
Chimaeras, or ratfishes, are the only extant group of holocephalan fishes and are the sole host group of gyrocotylidean cestodes, which represent a sister group of the true tapeworms (Eucestoda). These unique, non-segmented cestodes have been known since the 1850s and multiple species and genera have been erected despite a general agreement that the delineation of species on the basis of morphology is effectively impossible. Thus, in the absence of molecular studies, the validity of gyrocotylid taxa and their specific host associations has remained highly speculative. Here we report the presence of Gyrocotyle spp. from rarely-caught deep-sea chimaeras collected in the North-East Atlantic, and describe two new species: G. haffii n. sp. from the bent-nose chimaera, Harriota raleighana Goode & Bean, and G. discoveryi n. sp. from the large-eyed rabbit fish, Hydrolagus mirabilis (Collett). Nuclear ribosomal sequence data were generated for individual parasites taken from different host species collected on different dates and from different localities and were combined with previously published sequences. Phylogenetic analyses supported the recognition of independent lineages and clusters, indicative of species, but were indecisive in recovering the root of the tree in analyses that included non-gyrocotylid outgroup taxa. The molecular data reveal variation not reflected in morphology and point to a complex picture of genetic divergence shaped by both isolation and migration in the deep-sea environment.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The attached file is the published pdf.NHM Repositor
Leader humility and knowledge sharing intentions: A serial mediation model
Purpose: This paper examines the influence of leader humility on knowledge sharing intention. Drawing on social exchange theory (SET), we test the direct and indirect mechanisms to explain the influence leader humility has on knowledge sharing intention.
Design/Methodology/Approach: A two-wave, time-lagged field study was conducted. We surveyed 252 professional employees from Australia.
Findings: Results show a significant direct, positive association between leader humility and knowledge sharing intention. While leader humility had a direct, positive association with affective trust in supervisor and work engagement, it did not directly impact on organizational citizenship behaviors directed toward the individual (OCB-I). There were three SET-related, serial mediators in the relationship between leader humility and knowledge sharing intention. These were affective trust, work engagement, and OCB-I.
Research Limitations/Implications: Future studies should collect multi-source data such as peers’ or supervisors’ ratings of the focal respondents’ work engagement, OCB-I, and knowledge sharing behaviors to augment single-source data. Future studies could adopt an affect theory of social exchange to further explore the relationships tested in this study.
Originality/Value: This study contributes to the affect SET and knowledge management literature on how leadership behaviors impact the intention to share knowledge. Our study highlights the preference of the willingness to share knowledge with their co-workers is mediated by affective trust in their immediate supervisors, work engagement, and OCB-I that are equally important as treating their subordinates with humility
Temperature-dependent electronic structure and ferromagnetism in the d=oo Hubbard model studied by a modfied perturbation theory
The infinite-dimensional Hubbard model is studied by means of a modified
perturbation theory. The approach reduces to the iterative perturbation theory
for weak coupling. It is exact in the atomic limit and correctly reproduces the
dispersions and the weights of the Hubbard bands in the strong-coupling regime
for arbitrary fillings. Results are presented for the hyper-cubic and an
fcc-type lattice. For the latter we find ferromagnetic solutions. The
filling-dependent Curie temperature is compared with the results of a recent
Quantum Monte Carlo study.Comment: RevTeX, 5 pages, 6 eps figures included, Phys. Rev. B (in press),
Ref. 16 correcte
EEG reinvestigations of visual statistical learning for faces, scenes, and objects
The objective of this ongoing, replication study is to understand temporal and spatial patterns in our environment by using the technique of electroencephalography (EEG). Visual statistical learning (VSL) helps us to understand conditional probabilities from our environments. This concept is why we know that chairs are located under tables, not above. The goal of this study is to understand whether participants can unconsciously associate pairs of items (faces, scenes, and objects) from their short-term memory. Strong pairs become more similar to each other, as compared to weak pairs, which become less similar. In the main task, participants saw items appear on the screen, on at a time, for 100ms each. Items directly followed each other without transitions. In the post-task, participants were asked to rate how familiar pairs of items were, using a sliding scale. There were three types of pairs presented: strong pairs where item B followed item A 100% of the time; weak pairs where item B followed item A 11% of the time; and foil pairs where item B followed item A 0% of the time. In conclusion, results are similar to the current study (n = 10) in that there are behavioral differences between strong vs. foil and strong vs. weak pairs
Conditional expectation with regularization for missing data imputation
Missing data frequently occurs in datasets across various domains, such as
medicine, sports, and finance. In many cases, to enable proper and reliable
analyses of such data, the missing values are often imputed, and it is
necessary that the method used has a low root mean square error (RMSE) between
the imputed and the true values. In addition, for some critical applications,
it is also often a requirement that the imputation method is scalable and the
logic behind the imputation is explainable, which is especially difficult for
complex methods that are, for example, based on deep learning. Based on these
considerations, we propose a new algorithm named "conditional
Distribution-based Imputation of Missing Values with Regularization" (DIMV).
DIMV operates by determining the conditional distribution of a feature that has
missing entries, using the information from the fully observed features as a
basis. As will be illustrated via experiments in the paper, DIMV (i) gives a
low RMSE for the imputed values compared to state-of-the-art methods; (ii) fast
and scalable; (iii) is explainable as coefficients in a regression model,
allowing reliable and trustable analysis, makes it a suitable choice for
critical domains where understanding is important such as in medical fields,
finance, etc; (iv) can provide an approximated confidence region for the
missing values in a given sample; (v) suitable for both small and large scale
data; (vi) in many scenarios, does not require a huge number of parameters as
deep learning approaches; (vii) handle multicollinearity in imputation
effectively; and (viii) is robust to the normally distributed assumption that
its theoretical grounds rely on
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