An Educational Partnership to Promote Lifelong Leaders

This study focuses on the impact of intentional teaching in the areas of interpersonal and professional skills for students in grades 5-8, and a collaboration between students in the EKU School of Business and Model Laboratory School to apply skills learned in order to develop leadership and entrepreneurial strategies. The study illustrates the positive relationship developed between Model and the EKU School of Business through trainings and events. A series of brief evaluation surveys and other school-level data were used to demonstrate the results of the year-long collaboration. Overall, results demonstrate how both groups of students benefited from the experience

The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.Comment: Accepted at ACM Computing Survey

Targeting population nutrition through municipal health and food policy: Implications of New York City\u27s experiences in regulatory obesity prevention

Obesity remains a major public health challenge across OECD countries and policy-makers globally require successful policy precedents. This paper analyzes New York City’s innovative experiences in regulatory approaches to nutrition. We combined a systematic documentary review and key informant interviews (n = 9) with individuals directly involved in nutrition policy development and decision-making. Thematic analysis was guided by Kingdon’s three-streams-model and the International Obesity Task Force’s evidence-based decision-making framework. Our findings indicate that decisive mayoral leadership spearheaded initial agenda-change and built executive capacity to support evidence-driven policy. Policy-makers in the executive branch recognized the dearth of evidence for concrete policy interventions, and made contributing to the evidence base an explicit goal. Their approach preferred decision-making through executive action and rules passed by the Board of Health that successfully banned trans-fats from food outlets, set institutional food standards, introduced menu labeling requirements for chain restaurants, and improved access to healthy foods for disadvantaged populations. Although the Health Department collaborated with the legislature on legal and programmatic food access measures, there was limited engagement with elected representatives and the community on regulatory obesity prevention. Our analysis suggests that this hurt the administration’s ability to successfully communicate the public health messages motivating these contentious proposals; contributing to unexpected opposition from food access and minority advocates, and fueling charges of executive overreach. Overall, NYC presents a case of expert-driven policy change, underpinned by evidence-based environmental approaches. The city’s experience demonstrates that there is scope to redefine municipal responsibilities for public health and that incremental change and contentious public discussion can impact social norms around nutrition

Physical properties of seven binary and higher-order multiple OB systems

Analyses of multi-epoch, high-resolution (~ 50000) optical spectra of seven early-type systems provided various important new insights with respect to their multiplicity. First determinations of orbital periods were made for HD 92206C (2.022 d), HD 112244 (27.665 d), HD 164438 (10.25 d), HD 123056A (~ 1314 d) and HD 123056B (< 2 d); the orbital period of HD 318015 could be improved (23.445975 d). Concerning multiplicity, a third component was discovered for HD 92206C by means of He I line profiles. For HD 93146A, which was hitherto assumed to be SB1, lines of a secondary component could be discerned. HD 123056 turns out to be a multiple system consisting of a high-mass component A (~ O8.5) displaying a broad He II 5411 A feature with variable radial velocity, and of an inner pair B (~ B0) with double He I lines. The binary HD 164816 was revisited and some of its system parameters were improved. In particular, we determined its systemic velocity to be -7 km/s, which coincides with the radial velocity of the cluster NGC 6530. This fact, together with its distance, suggests the cluster membership of HD 164816. The OB system HD 318015 (V1082 Sco) belongs to the rare class of eclipsing binaries with a supergiant primary (B0.5/0.7). Our combined orbital and light-curve analysis suggests that the secondary resembles an O9.5III star. Our results for a limited sample corroborate the findings that many O stars are actually massive multiple systems.Comment: 16 pages, 16 figures, to appear in Astronomy and Astrophysic

Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information

In recent years, reinforcement learning (RL) has gained increasing attention in control engineering. Especially, policy gradient methods are widely used. In this work, we improve the tracking performance of proximal policy optimization (PPO) for arbitrary reference signals by incorporating information about future reference values. Two variants of extending the argument of the actor and the critic taking future reference values into account are presented. In the first variant, global future reference values are added to the argument. For the second variant, a novel kind of residual space with future reference values applicable to model-free reinforcement learning is introduced. Our approach is evaluated against a PI controller on a simple drive train model. We expect our method to generalize to arbitrary references better than previous approaches, pointing towards the applicability of RL to control real systems

Combining morphological and genomic evidence to resolve species diversity and study speciation processes of the Pallenopsis patagonica (Pycnogonida) species complex

Background: Pallenopsis patagonica (Hoek, 1881) is a morphologically and genetically variable sea spider species whose taxonomic classification is challenging. Currently, it is considered as a species complex including several genetic lineages, many of which have not been formally described as species. Members of this species complex occur on the Patagonian and Antarctic continental shelves as well as around sub-Antarctic islands. These habitats have been strongly influenced by historical large-scale glaciations and previous studies suggested that communities were limited to very few refugia during glacial maxima. Therefore, allopatric speciation in these independent refugia is regarded as a common mechanism leading to high biodiversity of marine benthic taxa in the high-latitude Southern Hemisphere. However, other mechanisms such as ecological speciation have rarely been considered or tested. Therefore, we conducted an integrative morphological and genetic study on the P. patagonica species complex to i) resolve species diversity using a target hybrid enrichment approach to obtain multiple genomic markers, ii) find morphological characters and analyze morphometric measurements to distinguish species, and iii) investigate the speciation processes that led to multiple lineages within the species complex. Results: Phylogenomic results support most of the previously reported lineages within the P. patagonica species complex and morphological data show that several lineages are distinct species with diagnostic characters. Two lineages are proposed as new species, P. aulaeturcarum sp. nov. Dömel & Melzer, 2019 and P. obstaculumsuperavit sp. nov. Dömel, 2019, respectively. However, not all lineages could be distinguished morphologically and thus likely represent cryptic species that can only be identified with genetic tools. Further, morphometric data of 135 measurements showed a high amount of variability within and between species without clear support of adaptive divergence in sympatry. Conclusions: We generated an unprecedented molecular data set for members of the P. patagonica sea spider species complex with a target hybrid enrichment approach, which we combined with extensive morphological and morphometric analyses to investigate the taxonomy, phylogeny and biogeography of this group. The extensive data set enabled us to delineate species boundaries, on the basis of which we formally described two new species. No consistent evidence for positive selection was found, rendering speciation in allopatric glacial refugia as the most likely model of speciation

Optical detection of single non-absorbing molecules using the surface plasmon of a gold nanorod

Current optical detection schemes for single molecules require light absorption, either to produce fluorescence or direct absorption signals. This severely limits the range of molecules that can be detected, because most molecules are purely refractive. Metal nanoparticles or dielectric resonators detect non-absorbing molecules by a resonance shift in response to a local perturbation of the refractive index, but neither has reached single-protein sensitivity. The most sensitive plasmon sensors to date detect single molecules only when the plasmon shift is amplified by a highly polarizable label or by a localized precipitation reaction on the particle's surface. Without amplification, the sensitivity only allows for the statistical detection of single molecules. Here we demonstrate plasmonic detection of single molecules in realtime, without the need for labeling or amplification. We monitor the plasmon resonance of a single gold nanorod with a sensitive photothermal assay and achieve a ~ 700-fold increase in sensitivity compared to state-of-the-art plasmon sensors. We find that the sensitivity of the sensor is intrinsically limited due to spectral diffusion of the SPR. We believe this is the first optical technique that detects single molecules purely by their refractive index, without any need for photon absorption by the molecule. The small size, bio-compatibility and straightforward surface chemistry of gold nanorods may open the way to the selective and local detection of purely refractive proteins in live cells