Free to choose:Mutualist motives for partner choice, proportional division, punishment, and help

Mutualism–the disposition to cooperate in ways that benefit both actor and recipient–has been proposed as a key construct in the evolution of cooperation, with distinct adaptations for 1) partner choice, 2) division, 3) punishment, and 4) helping. However, no psychological validation of this 4-fold psychological structure exists, and no measure of the trait is available. To fill this need, in two pre-registered studies (total N = 902), we: (A) Develop and administer items assessing each of the four mutualist adaptations; (B) Show good fit to the predicted four factor model; (C) Demonstrate reliability and stability across time; (D) Evidence discriminant validity from existing constructs, including compassion and utilitarianism; (E) Establish external validity by predicting proportional choices in catch division, opposition to partner coercion, and reduced support for redistribution; and (F) Replicate each of these findings. Jointly, these results support the validity of mutualism, including a motive to maintain the freedom to choose, and provide reliable scales for use in integrating, further developing, and applying mutualism

Support for redistribution is shaped by motives of egalitarian division and coercive redistribution

The three-player evolutionary model of support for redistribution is compatible with a fairness motive; however, existing research has found near-zero effects of fairness. Here we propose an egalitarian division fairness motive, solving the problem of reward for collaboration and impacting support for redistribution. Study 1 (N = 403) showed egalitarian division fairness had additional predictive power predicting support for redistribution (β = 0.14), as well as discriminant validity from self-interest, compassion, and envy. Robustness was supported by a replication (N = 402), yielding a significant and larger effect size (β = 0.25) of egalitarian division with support for redistribution. We also examined support for coercive redistribution. In both studies, willingness to use coercive redistribution was predicted by egalitarian division fairness (S1 β = 0.15, S2: β = 0.31) and, independently, by instrumental harm (S1 β = 0.21, S2: β = 0.16). These motives expand the three-player model to include fairness and coercive enforcement, and suggest applications of evolution in developing better political, economic, and ethical knowledge. Evolved motives accounted for ~45 % of support for redistribution

AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing

Cycling stress assessment, which quantifies cyclists' perceived stress imposed by the built environment and motor traffics, increasingly informs cycling infrastructure planning and cycling route recommendation. However, currently calculating cycling stress is slow and data-intensive, which hinders its broader application. In this paper, We propose a deep learning framework to support accurate, fast, and large-scale cycling stress assessments for urban road networks based on street-view images. Our framework features i) a contrastive learning approach that leverages the ordinal relationship among cycling stress labels, and ii) a post-processing technique that enforces spatial smoothness into our predictions. On a dataset of 39,153 road segments collected in Toronto, Canada, our results demonstrate the effectiveness of our deep learning framework and the value of using image data for cycling stress assessment in the absence of high-quality road geometry and motor traffic data

A Machine Learning Approach to Solving Large Bilevel and Stochastic Programs: Application to Cycling Network Design

We present a novel machine learning-based approach to solving bilevel programs that involve a large number of independent followers, which as a special case include two-stage stochastic programming. We propose an optimization model that explicitly considers a sampled subset of followers and exploits a machine learning model to estimate the objective values of unsampled followers. Unlike existing approaches, we embed machine learning model training into the optimization problem, which allows us to employ general follower features that can not be represented using leader decisions. We prove bounds on the optimality gap of the generated leader decision as measured by the original objective function that considers the full follower set. We then develop follower sampling algorithms to tighten the bounds and a representation learning approach to learn follower features, which can be used as inputs to the embedded machine learning model. Using synthetic instances of a cycling network design problem, we compare the computational performance of our approach versus baseline methods. Our approach provides more accurate predictions for follower objective values, and more importantly, generates leader decisions of higher quality. Finally, we perform a real-world case study on cycling infrastructure planning, where we apply our approach to solve a network design problem with over one million followers. Our approach presents favorable performance compared to the current cycling network expansion practices

The Risk of Thromboembolic Complications in Fontan Patients with Atrial Flutter/fibrillation Treated with Electrical Cardioversion

Atrial flutter or fibrillation (AFF) remains a major chronic complication of the Fontan procedure. This complication further predisposes this patient population to thromboembolic events. However, the incidence of thromboembolic complications in Fontan patients with AFF prior to or acutely after electrical cardioversion is unknown. This study aimed to characterize the risk of post-cardioversion thromboembolic events in this population. We performed a retrospective medical record review of all patients with a history of Fontan operation treated with direct current cardioversion for AFF at Riley Children’s Hospital between June 1992 and March 2014. A total of 57 patients were identified and reviewed. A total of 216 episodes of AFF required electrical cardioversion. Patients were treated with anticoagulation/antiplatelet therapy in 86.1 % (N = 186) of AFF episodes. Right atrial or Fontan conduit clots were observed in 33 patients (57.9 %) with 61 episodes of AFF. Approximately half (49.2 %, N = 30) of these episodes were treated immediately with electrical cardioversion. Twenty-five of 33 (75.8 %) patients with intracardiac thrombi had an atriopulmonary Fontan. Five (15.2 %) patients with a lateral caval tunnel had clots in the Fontan conduit, and three (9.1 %) patients with right atrium to right ventricular outflow tract (RVOT) connections presented with right atrial mural thrombi. Nine of the 57 (15.8 %) patients had documented stroke, and three (5.3 %) patients had pulmonary emboli during follow-up, although none of these emboli were associated with electrical cardioversion. The risk of thrombus and thromboembolism associated with AFF is high in the Fontan population. However, the risk of thromboembolism associated with cardioversion in the setting of anticoagulation is very low

Internal tidal modal ray refraction and energy ducting in baroclinic Gulf Stream currents

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 48 (2018): 1969-1993, doi:10.1175/JPO-D-18-0031.1.Upstream mean semidiurnal internal tidal energy flux has been found in the Gulf Stream in hydrodynamical model simulations of the Atlantic Ocean. A major source of the energy in the simulations is the south edge of Georges Bank, where strong and resonant Gulf of Maine tidal currents are found. An explanation of the flux pattern within the Gulf Stream is that internal wave modal rays can be strongly redirected by baroclinic currents and even trapped (ducted) by current jets that feature strong velocities above the thermocline that are directed counter to the modal wavenumber vector (i.e., when the waves travel upstream). This ducting behavior is analyzed and explained here with ray-based wave propagation studies for internal wave modes with anisotropic wavenumbers, as occur in mesoscale background flow fields. Two primary analysis tools are introduced and then used to analyze the strong refraction and ducting: the generalized Jones equation governing modal properties and ray equations that are suitable for studying waves with anisotropic wavenumbers.The Woods Hole research was supported by National Science Foundation Grant OCE-1060430 and by the Office of Naval Research Grants N00014-11-1-0701 and N00014-17-1-2624. The USM research was supported by ONR Grant N00014-15-1-2288 and National Science Foundation Grant OCE-1537449.2019-02-2

Tailoring Capture-Recapture Methods to Estimate Registry-Based Case Counts Based on Error-Prone Diagnostic Signals

Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database