Anomalous scaling behavior in Takens-Bogdanov bifurcations

A general algorithm is presented for estimating the nonlinear instability threshold, $\sigma_c$, for subcritical transitions in systems where the linearized dynamics is significantly non-normal (i.e. subcritical bifurcations of {\em Takens-Bogdanov} type). The $N$-dimensional degenerate node is presented as an example. The predictions are then compared to numerical studies with excellent agreement.Comment: 6 page

Employed parents\u27 satisfaction with food choice coping strategies: Influence of gender and structure

This study aimed to understand parents\u27 evaluations of the way they integrated work-family demands to manage food and eating. Employed, low/moderate-income, urban, U.S., Black, White, and Latino mothers (35) and fathers (34) participated in qualitative interviews exploring work and family conditions and spillover, food roles, and food-choice coping and family-adaptive strategies. Parents expressed a range of evaluations from overall satisfaction to overall dissatisfaction as well as dissatisfaction limited to work, family life, or daily schedule. Evaluation criteria differed by gender. Mothers evaluated satisfaction on their ability to balance work and family demands through flexible home and work conditions, while striving to provide healthy meals for their families. Fathers evaluated satisfaction on their ability to achieve schedule stability and participate in family meals, while meeting expectations to contribute to food preparation. Household, and especially work structural conditions, often served as sizeable barriers to parents fulfilling valued family food roles. These relationships highlight the critical need to consider the intersecting influences of gender and social structure as influences on adults\u27 food choices and dietary intake and to address the challenges of work and family integration among low income employed parents as a way to promote family nutrition in a vulnerable population

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences

Behavioral Contexts, Food-Choice Coping Strategies, and Dietary Quality of a Multiethnic Sample of Employed Parents

Employed parents\u27 work and family conditions provide behavioral contexts for their food choices. Relationships between employed parents\u27 food-choice coping strategies, behavioral contexts, and dietary quality were evaluated. Data on work and family conditions, sociodemographic characteristics, eating behavior, and dietary intake from two 24-hour dietary recalls were collected in a random sample cross-sectional pilot telephone survey in the fall of 2006. Black, white, and Latino employed mothers (n=25) and fathers (n=25) were recruited from a low/moderate income urban area in upstate New York. Hierarchical cluster analysis (Ward\u27s method) identified three clusters of parents differing in use of food-choice coping strategies (ie, Individualized Eating, Missing Meals, and Home Cooking). Cluster sociodemographic, work, and family characteristics were compared using χ2 and Fisher\u27s exact tests. Cluster differences in dietary quality (Healthy Eating Index 2005) were analyzed using analysis of variance. Clusters differed significantly (P≤0.05) on food-choice coping strategies, dietary quality, and behavioral contexts (ie, work schedule, marital status, partner\u27s employment, and number of children). Individualized Eating and Missing Meals clusters were characterized by nonstandard work hours, grabbing quick food instead of a meal, using convenience entrées at home, and missing meals or individualized eating. The Home Cooking cluster included considerably more married fathers with nonemployed spouses and more home-cooked family meals. Food-choice coping strategies affecting dietary quality reflect parents\u27 work and family conditions. Nutritional guidance and family policy needs to consider these important behavioral contexts for family nutrition health

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 page

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems