54 research outputs found
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
Fine-Tuning of the Yukawa and Quartic Couplings in Supersymmetric QCD
In this work, we investigate the fine tuning of parameters in 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 , the
gauge parameter , and the gauge coupling . 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
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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 volunteersâ 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
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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
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