Limits of Ordered Graphs and their Applications

The emerging theory of graph limits exhibits an analytic perspective on graphs, showing that many important concepts and tools in graph theory and its applications can be described more naturally (and sometimes proved more easily) in analytic language. We extend the theory of graph limits to the ordered setting, presenting a limit object for dense vertex-ordered graphs, which we call an \emph{orderon}. As a special case, this yields limit objects for matrices whose rows and columns are ordered, and for dynamic graphs that expand (via vertex insertions) over time. Along the way, we devise an ordered locality-preserving variant of the cut distance between ordered graphs, showing that two graphs are close with respect to this distance if and only if they are similar in terms of their ordered subgraph frequencies. We show that the space of orderons is compact with respect to this distance notion, which is key to a successful analysis of combinatorial objects through their limits. We derive several applications of the ordered limit theory in extremal combinatorics, sampling, and property testing in ordered graphs. In particular, we prove a new ordered analogue of the well-known result by Alon and Stav [RS\&A'08] on the furthest graph from a hereditary property; this is the first known result of this type in the ordered setting. Unlike the unordered regime, here the random graph model $G(n, p)$ with an ordering over the vertices is \emph{not} always asymptotically the furthest from the property for some $p$. However, using our ordered limit theory, we show that random graphs generated by a stochastic block model, where the blocks are consecutive in the vertex ordering, are (approximately) the furthest. Additionally, we describe an alternative analytic proof of the ordered graph removal lemma [Alon et al., FOCS'17].Comment: Added a new application: An Alon-Stav type result on the furthest ordered graph from a hereditary property; Fixed and extended proof sketch of the removal lemma applicatio

Hard Properties with (Very) Short PCPPs and Their Applications

We show that there exist properties that are maximally hard for testing, while still admitting PCPPs with a proof size very close to linear. Specifically, for every fixed ?, we construct a property P^(?)? {0,1}^n satisfying the following: Any testing algorithm for P^(?) requires ?(n) many queries, and yet P^(?) has a constant query PCPP whose proof size is O(n?log^(?)n), where log^(?) denotes the ? times iterated log function (e.g., log^(2)n = log log n). The best previously known upper bound on the PCPP proof size for a maximally hard to test property was O(n?polylog(n)). As an immediate application, we obtain stronger separations between the standard testing model and both the tolerant testing model and the erasure-resilient testing model: for every fixed ?, we construct a property that has a constant-query tester, but requires ?(n/log^(?)(n)) queries for every tolerant or erasure-resilient tester

Specification and implementation of mapping rule visualization and editing : MapVOWL and the RMLEditor

Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values. (C) 2018 Elsevier B.V. All rights reserved

Engaging African American Men as Citizen Scientists to Validate a Prostate Cancer Biomarker: Work-in-Progress

African American men (AAM) are underrepresented in prostate cancer (PCa) research despite known disparities. Screening with prostate-specific antigen (PSA) has low specificity for high-grade PCa leading to PCa over diagnosis. The Prostate Health Index (PHI) has higher specificity for lethal PCa but needs validation in AAM. Engaging AAM as citizen scientists (CSs) may improve participation of AAM in PCa research

Boosted Off-Policy Learning

We investigate boosted ensemble models for off-policy learning from logged bandit feedback. Toward this goal, we propose a new boosting algorithm that directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the empirical risk decreases (possibly exponentially fast) with each round of boosting, provided a "weak" learning condition is satisfied. We further show how the base learner reduces to standard supervised learning problems. Experiments indicate that our algorithm can outperform deep off-policy learning and methods that simply regress on the observed rewards, thereby demonstrating the benefits of both boosting and choosing the right learning objective

Society of Behavior Medicine (SBM) Urges Congress to Ensure Affordable Care Act Coverage of Prostate Cancer Screening Support Services for High-Risk Men

Prostate cancer (PCa) disproportionately affects African American men. Early detection reduces risk of mortality. The United States Preventive Services Task Force (USPSTF) issued an updated recommendation statement on serum Prostate Specific Antigen (PSA)-based screening for PCa. Specifically, in 2012, the USPSTF recommended against PSA-based screening due to risk for overdiagnosis and overtreatment. However, the updated 2018 guidelines recommend consideration of screening for certain at risk men and revised the recommendation rating from “D” to “C.” This new guideline recommends providers to educate high-risk men on the benefits and harms of PSA-based PCa screening so that they can make an informed decision. The Affordable Care Act (ACA) includes provisions of service coverage for patient navigators who can help patients decide whether screening is appropriate, given potential risks and benefits, and training of health care providers in shared-decision regarding screening/treatment. These services can be utilized to support health care providers to better adhere to the new guideline. However, recommendations that are given a C rating or lower are not consistently reimbursed through many plans, including those offered through the ACA marketplace. The Society of Behavioral Medicine (SBM) supports the USPSTF guideline for the consideration of prostate cancer screening for high-risk men between the ages of 55 and 69. SBM encourages policymakers to include provisions for coverage of patient navigation services in the ACA to facilitate shared decision-making between providers and patients regarding screening

Preliminary Evaluation of a Citizen Scientist Educational Curriculum Aimed at Engaging Black Men in Lung Cancer Early Detection Screening

This article describes an educational program to engage African American men as citizen scientists (CSs) and future research partners in a lung cancer screening project. We provide an overview of the curriculum used, the structure and format of the educational sessions, and associated educational outcomes. Furthermore, we describe lessons learned in the engagement of African American men as CS in community-based lung-health equity research. The CS educational program included five group-based sessions delivered through zoom. The educational curriculum was adapted from the University of Florida Citizen Scientist program and tailored to address lung health and the contextual experiences of African American men. Each session lasted 90 minutes. Pre- and post-test measures were collected to examine changes in knowledge, comfort, health literacy, research interests, and medical mistrust. Eight African American men completed the CS educational program. Attendance rates were high for each session (100%). Seven participants completed additional human subject research certification. Improvements were observed from pre- to post-test in participants’ level of knowledge, comfort, and health literacy but not medical mistrust. CS reported the most interest in participating in research aimed to identify important community strengths and problems. Study findings suggest that it was feasible to deliver an online citizen scientist educational program designed to prepare participants to serve as partners in a lung cancer screening intervention for African American men. Results suggest the educational program has the potential to improve key outcomes including completion of regulatory training and increased research-related knowledge, comfort, and health literacy