Bi-directional relations between maternal anxiety and infant affect-biased attention across the first 24-months of life

Introduction: Patterns of affect-biased attention are related to anxiety and anxiety risk. However, little is known regarding how affect-biased attention develops. Recent work suggests relations with both infant temperamental negative affect and maternal anxiety. The current paper examines potential bi-directional relations between infant attention, infant negative affect, and maternal anxiety to better understand a developmental process that may precede the emergence of anxiety. Method: Infant-mother pairs (N = 333) participated in a multi-site, longitudinal study providing eye-tracking and questionnaire data when infants were 4-, 8-, 12-, 18- and 24-months. A random intercepts cross-lag panel model assessed bi-directional relations between infant attention, infant negative affect and maternal anxiety.Results: Within-person deviations in maternal anxiety were prospectively, negatively related to within-person deviations in infant attention to angry face configurations at every assessment and within-person deviations in infant attention to happy face configurations at the final two assessments. Additionally, within-person deviations in infant negative affect were prospectively, positively related to within-person deviations in infant attention to angry face configurations at 12- and 18-months. Consistent bi-directional relations were not found.Conclusion: Our results suggest that infants do not display a stable bias to threat in the first 24 months of life. Rather, individual differences, in this case maternal anxiety and infant negative affect, shape patterns of attention biases over time. The current results provide an initial understanding of bi-directional relations in affect-biased attention development.

Learning Directional Sentence-Pair Embedding for Natural Language Reasoning (Student Abstract)

Enabling the models with the ability of reasoning and inference over text is one of the core missions of natural language understanding. Despite deep learning models have shown strong performance on various cross-sentence inference benchmarks, recent work has shown that they are leveraging spurious statistical cues rather than capturing deeper implied relations between pairs of sentences. In this paper, we show that the state-of-the-art language encoding models are especially bad at modeling directional relations between sentences by proposing a new evaluation task: Cause-and-Effect relation prediction task. Back by our curated Cause-and-Effect Relation dataset (Cℰℛ), we also demonstrate that a mutual attention mechanism can guide the model to focus on capturing directional relations between sentences when added to existing transformer-based models. Experiment results show that the proposed approach improves the performance on downstream applications, such as the abductive reasoning task.

Bi-directional relations between stress and self-reported and actigraphy-assessed sleep: a daily intensive longitudinal study

Study Objectives Stress is associated with poor and short sleep, but the temporal order of these variables remains unclear. This study examined the temporal and bi-directional associations between stress and sleep and explored the moderating role of baseline sleep complaints, using daily, intensive longitudinal designs. Methods Participants were 326 young adults (Mage = 23.24 ± 5.46), providing >2,500 nights of sleep altogether. Prospective total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were measured using actigraphy and sleep diaries. Perceived stress was reported three times daily between: 11:00–15:00, 15:30–19:30, and 20:00–02:00. Sleep complaints were measured at baseline using the PROMIS sleep disturbance scale. Within- and between-person sleep and stress variables were tested using cross-lagged multilevel models. Results Controlling for covariates and lagged outcomes, within-person effects showed that higher evening stress predicted shorter actigraphic and self-reported TST (both p < .01). Conversely, shorter actigraphic and self-reported TST predicted higher next-day stress (both p < .001). Longer self-reported SOL and WASO (both p < .001), as well as lower actigraphic (p < .01) and self-reported SE (p < .001), predicted higher next-day stress. Between-person effects emerged only for self-reported TST predicting stress (p < .01). No significant results were found for the moderating role of baseline sleep complaints. Conclusions Results demonstrated bi-directional relations between stress and sleep quantity, and a consistent direction of worse sleep quantity and continuity predicting higher next-day stress. Results highlighted within-individual daily variation as being more important than between-individual differences when examining sleep and daytime functioning associations.

Temporal and Vertical Relations between Various Environmental Factors in the Largest Lake of Łęczna-Włodawa Lake District (Eastern Poland)

Due to global warming and increasing water eutrophication, understanding in-lake relationships is paramount to prevent excessive pollution and further negative changes in lakes. The physico-chemical and biological parameters, as well as nutrient variabilities, both temporal and vertical (in the water column), were studied in the largest Łęczna-Włodawa lake. The study was conducted during two consecutive water years, from November 2015 to September 2017. The non-parametric Kruskal–Wallis ANOVA, test H was used to detect variability between years, and among seasons and lake depths in water temperature, electrical conductivity, pH, ORP, concentration of dissolved oxygen as well as concentration of NO3−, PO43−, TP and chlorophyll a. One-way analysis of variance (ANOVA) was used to detect any differences in Secchi depth (SD) and concentration of NH4+, NO2−, and TN. Ordination techniques: Detrended correspondence analysis (DCA), and Principal Components Analysis (PCA) were used to describe the relationships among 13 environmental variables and distribution of samples. Results showed significant differences (P < 0.05) between two years of the study for most variables except of water temperature, visibility, concentration of PO43−, TN and TP, which corresponded with highly different weather conditions in both years. All of the tested variables, except for nutrients, differed significantly among seasons. Most parameters showed fluctuation in depth, especially in summer and in winter, despite the fact that no hypolimnion or long-lasting thermocline was observed in the study period. However, variability of Chl-a was statistically significant only. The results emphasized complex and multi-directional relations between tested variables, dependent on both external (catchment) and internal (in-lake) processes.

A Geospatial Application Framework for Directional Relations

Geographic data analysis is based on the use of spatial relations as a means of selecting and processing geometric data associated with geographic features. Starting from 1990, topological relations have been recognized as fundamental criteria in geographic data processing, leaving out other kinds of spatial relations, such as directional relations. The latter ones, despite having quite an important role in geospatial applications, have been developed as theoretical models but very little implemented in systems. We refer in this paper to the 5-intersection model for expressing projective relations that can be used to implement directional relations in various frames of reference. We design an application framework in Java and use the framework for answering various categories of queries involving directions. We finally outline how to use the framework for validating the cognitive adequacy of relations with user tests.

On Spatial Relations for Non-small Cells Lungs Cancer Interpretation

Computers and artificial intelligence affect every field of life nowadays. In medical image interpretation automatic decision making using algorithms are used increasingly in every sub-field and computer aided diagnosis (CAD) is one of the main tools available to medical science today. CAD systems are used as an augmented option for both the medical practitioner and the patients, with image analysis and interpretation being of primary importance. In particular, spatial relations are used in knowledge representation, and these relations can be used for effective medical image interpretation. In this paper, we put forth an algorithm for defining non-small cells lungs cancer (NSCLC) stages in lungs images interpretation using topological spatial relations. We show an application case study in event motion predictions for lung cancer staging scoring - tumor, nodes and metastasis (TNM) - with combined topological and directional relations.

Planar periodic orbits’ construction around libration points with invariant manifold technique

This paper revisits the planar periodic motions around libration points in circular restricted three-body problem based on invariant manifold technique. The invariant manifold technique is applied to construct the nonlinear polynomial relations between ξ-direction and η-direction of a small celestial body during its periodic motion. Such direct nonlinear relations reduce the dimension of the dynamical system and facilitate convenient approximate analytical solutions. The nonlinear directional relations also provide terminal constraints for computing periodic motions. The method to construst periodic orbits proposed in this study presents a new point of view to explore the orbital dynamics. As an application in numerical simulations, nonlinear relations are adopted as topological terminal constraints to construct the periodic orbits with differential correction procedure. Numerical examples verify the validity of the proposed method for both collinear and triangular libration cases.