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  1. Free, publicly-accessible full text available June 1, 2025
  2. Free, publicly-accessible full text available February 1, 2025
  3. Meta-analyses have not shown emotions to be significant predictors of deception. Criticisms of this conclusion argued that individuals must be engaged with each other in higher stake situations for such emotions to manifest, and that these emotions must be evaluated in their verbal context (Frank and Svetieva in J Appl Res Memory Cognit 1:131–133, 10.1016/j.jarmac.2012.04.006, 2012). This study examined behavioral synchrony as a marker of engagement in higher stakes truthful and deceptive interactions, and then compared the differences in facial expressions of fear, contempt, disgust, anger, and sadness not consistent with the verbal content. Forty-eight pairs of participants were randomly assigned to interviewer and interviewee, and the interviewee was assigned to steal either a watch or a ring and to lie about the item they stole, and tell the truth about the other, under conditions of higher stakes of up to $30 rewards for successful deception, and $0 plus having to write a 15-min essay for unsuccessful deception. The interviews were coded for expression of emotions using EMFACS (Friesen and Ekman in EMFACS-7; emotional facial action coding system, 1984). Synchrony was demonstrated by the pairs of participants expressing overlapping instances of happiness (AU6 + 12). A 3 (low, moderate, high synchrony) × 2 (truth, lie) mixed-design ANOVA found that negative facial expressions of emotion were a significant predictor of deception, but only when they were not consistent with the verbal content, in the moderate and high synchrony conditions. This finding is consistent with data and theorizing that shows that with higher stakes, or with higher engagement, emotions can be a predictor of deception. 
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    Free, publicly-accessible full text available December 16, 2024
  4. Abstract Strong gravitational lensing of gravitational wave sources offers a novel probe of both the lens galaxy and the binary source population. In particular, the strong lensing event rate and the time-delay distribution of multiply imaged gravitational-wave binary coalescence events can be used to constrain the mass distribution of the lenses as well as the intrinsic properties of the source population. We calculate the strong lensing event rate for a range of second- (2G) and third-generation (3G) detectors, including Advanced LIGO/Virgo, A+, Einstein Telescope (ET), and Cosmic Explorer (CE). For 3G detectors, we find that ∼0.1% of observed events are expected to be strongly lensed. We predict detections of ∼1 lensing pair per year with A+, and ∼50 pairs per year with ET/CE. These rates are highly sensitive to the characteristic galaxy velocity dispersion, σ * , implying that observations of the rates will be a sensitive probe of lens properties. We explore using the time-delay distribution between multiply imaged gravitational-wave sources to constrain properties of the lenses. We find that 3G detectors would constrain σ * to ∼21% after 5 yr. Finally, we show that the presence or absence of strong lensing within the detected population provides useful insights into the source redshift and mass distribution out to redshifts beyond the peak of the star formation rate, which can be used to constrain formation channels and their relation to the star formation rate and delay-time distributions for these systems. 
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  5. Abstract. End-member mixing analysis (EMMA) is a method of interpreting stream water chemistry variations and is widely used for chemical hydrograph separation. It is based on the assumption that stream water is a conservative mixture of varying contributions from well-characterized source solutions (end-members). These end-members are typically identified by collecting samples of potential end-member source waters from within the watershed and comparing these to the observations. Here we introduce a complementary data-driven method (convex hull end-member mixing analysis – CHEMMA) to infer the end-member compositions and their associated uncertainties from the stream water observations alone. The method involves two steps. The first uses convex hull nonnegative matrix factorization (CH-NMF) to infer possible end-member compositions by searching for a simplex that optimally encloses the stream water observations. The second step uses constrained K-means clustering (COP-KMEANS) to classify the results from repeated applications of CH-NMF and analyzes the uncertainty associated with the algorithm. In an example application utilizing the 1986 to 1988 Panola Mountain Research Watershed dataset, CHEMMA is able to robustly reproduce the three field-measured end-members found in previous research using only the stream water chemical observations. CHEMMA also suggests that a fourth and a fifth end-member can be (less robustly) identified. We examine uncertainties in end-member identification arising from non-uniqueness, which is related to the data structure, of the CH-NMF solutions, and from the number of samples using both real and synthetic data. The results suggest that the mixing space can be identified robustly when the dataset includes samples that contain extremely small contributions of one end-member, i.e., samples containing extremely large contributions from one end-member are not necessary but do reduce uncertainty about the end-member composition. 
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