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

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