The role fathers play in the lives of their children is, as any behavior, dependent on their knowledge of factors influencing the health and safety of children and the societal context in which those fathers live, work, and worship. In the context of maternal and child health, the role of supportive partners of the mother dramatically strengthens the ability of the mother to set and achieve appropriate early life goals for their child, including wise nutritional choices and infant safety. Reaching Our Brothers Everywhere (ROBE) developed in 2014 as an independently integrated program of Reaching Our Sisters Everywhere, Inc. (ROSE), with the mission of educating, equipping, and empowering men to assist in the ROSE effort of increasing breastfeeding initiation and duration rates, primarily in the Black community, by educating men of the numerous health, nutritional, social, and economic benefits of breastfeeding. Similarly, through education about the national Safe Sleep campaign, ROBE hopes to assist in the mitigation of infant mortality rates. Then, this article seeks to elucidate the modern Black Fatherhood Movement, in which ROBE observes and interacts, and to lay out axioms from the natural and social sciences that will allow us to achieve strengthened American family structures through transformative change, using the medium of Fatherhood work.
Unlike matrix completion, no algorithm for the tensor completion problem has so far been shown to achieve the informationtheoretic sample complexity rate. This paper develops a new algorithm for the special case of completion for nonnegative tensors. We prove that our algorithm converges in a linear (in numerical tolerance) number of oracle steps, while achieving the informationtheoretic rate. Our approach is to define a new norm for nonnegative tensors using the gauge of a specific 0-1 polytope that we construct. Because the norm is defined using a 0-1 polytope, this means we can use integer linear programming to solve linear separation problems over the polytope. We combine this insight with a variant of the Frank-Wolfe algorithm to construct our numerical algorithm, and we demonstrate its effectiveness and scalability through experiments.
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