On data skewness, stragglers, and MapReduce progress indicators

We tackle the problem of predicting the performance of MapReduce applications, designing accurate progress indicators that keep programmers informed on the percentage of completed computation time during the execution of a job. Through extensive experiments, we show that state-of-the-art progress indicators (including the one provided by Hadoop) can be seriously harmed by data skewness, load unbalancing, and straggling tasks. This is mainly due to their implicit assumption that the running time depends linearly on the input size. We thus design a novel profile-guided progress indicator, called NearestFit, that operates without the linear hypothesis assumption and exploits a careful combination of nearest neighbor regression and statistical curve fitting techniques. Our theoretical progress model requires fine-grained profile data, that can be very difficult to manage in practice. To overcome this issue, we resort to computing accurate approximations for some of the quantities used in our model through space- and time-efficient data streaming algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive empirical assessment over the Amazon EC2 platform on a variety of real-world benchmarks shows that NearestFit is practical w.r.t. space and time overheads and that its accuracy is generally very good, even in scenarios where competitors incur non-negligible errors and wide prediction fluctuations. Overall, NearestFit significantly improves the current state-of-art on progress analysis for MapReduce

Renormalization of the Yukawa and Quartic Couplings in N=1\mathcal{N} = 1 Supersymmetric QCD

In this work we perform calculations in order to determine the renormalization factors and the mixing coefficients of the Yukawa and the quartic couplings in $\mathcal{N} = 1$ Supersymmetric QCD. The Yukawa couplings describe the interactions between gluino, quark and squark fields whereas the quartic couplings describe four-squark interactions. We discretize the action on a Euclidean lattice using the Wilson formulation for the gluino, quark and gluon fields; for squark fields (scalar fields) we employ na\"ive discretization. At the quantum level Yukawa and quartic interactions suffer from mixing with other operators which have the same transformation properties. Exploiting parity and charge conjugation symmetries of the Supersymmetric QCD action, we reduce the allowed mixing patterns. We compute, perturbatively to one-loop and to the lowest order in the lattice spacing, the relevant three-point Green's functions so as to fine tune the Yukawa couplings and the relevant four-point Green's functions to fine tune the quartic couplings. We use both dimensional and lattice regularizations as required for implementing the Modified Minimal Subtraction scheme ($\overline{\rm MS}$)

Fine-Tuning of the Yukawa and Quartic Couplings in Supersymmetric QCD

In this work, we investigate the fine tuning of parameters in $\mathcal{N} = 1$ Supersymmetric QCD, discretized on a Euclidean lattice. Specifically, we study the renormalization of the Yukawa (gluino-quark-squark interactions) and the quartic (four-squark interactions) couplings. At the quantum level, these interactions suffer from mixing with other operators which have the same transformation properties. We exploit the symmetries of the action, such as charge conjugation and parity, in order to reduce the allowed mixing patterns. To deduce the renormalizations and the mixing coefficients we compute, perturbatively to one-loop and to the lowest order in the lattice spacing, the relevant three-point and four-point Green's functions using both dimensional and lattice regularizations. Our lattice formulation involves the Wilson discretization for the gluino and quark fields; for gluons we employ the Wilson gauge action; for scalar fields (squarks) we use na\"ive discretization. We obtain analytic expressions for the renormalization and mixing coefficients of the Yukawa couplings; they are functions of the number of colors $N_c$, the gauge parameter $\alpha$, and the gauge coupling $g$. Furthermore, preliminary results on the quartic couplings are also presented.Comment: 9 pages, 2 figures, 2 tables, Proceedings of the 39th International Symposium on Lattice Field Theory, 8th-13th August, 2022, Rheinische Friedrich-Wilhelms-Universit\"at Bonn, Bonn, German

Youth environmental science learning and agency: A unifying lens across community and citizen science settings

This study addresses an existing gap in our understanding of how participation in environmental Community and Citizen Science (CCS) projects may impact young volunteers’ environmental science learning across a wide variety of settings. We examined youth learning across four settings which we represented as cases: 5 short-term field-based events (BioBlitzes), 3 longer-term field-based monitoring programs, fully online projects (Zooniverse), and a hybrid format that combines participation in the field and online spaces (iNaturalist). This multiple-case study uses the Environmental Science Agency framework to interpret learning evidence of 33 young CCS volunteers (aged 10-13 years) in post-participation surveys, semi-structured interviews, and in ethnographic field notes for the field-based participants. Across the cases, we found particular features of the CCS projects and the scientific framings that may have encouraged aspects of ESA. Design features such as access to new knowledge, training, and scientific tools provided by the CCS projects encouraged youth to learn rich and varied understandings of disciplinary content, scientific skills and practices. An increased sense of confidence and competence in youth around the scientific practices of the projects were stimulated by scientific framing of CSS and ongoing participation. Overall, these aspects also supported small manifestations of youth agency with science

Learning Opportunities and Outcomes in Citizen Science: A Heuristic Model for Design and Evaluation

Growing numbers of Citizen Science (CS) projects focus on learning about science through the collaboration of professional scientists and citizen scientists. However, resources for the design and evaluation of CS projects in terms of learning about science are scarce. Therefore, this chapter aims to provide a model for the heuristic analysis of the supply and use of learning opportunities in CS and apply it to different CS projects. We hope that the design of future CS projects considers the MODEL-CS as an approach to enable as many participants with different prerequisites as possible to take advantage of the learning opportunities provided