Underlying event sensitive observables in Drell-Yan production using GENEVA

We present an extension of the GENEVA Monte Carlo framework to include multiple parton interactions (MPI) provided by PYTHIA8. This allows us to obtain predictions for underlying-event sensitive measurements in Drell-Yan production, in conjunction with GENEVA's fully-differential NNLO calculation, NNLL' resummation for the 0-jet resolution variable (beam thrust), and NLL resummation for the 1-jet resolution variable. We describe the interface with the parton shower algorithm and MPI model of PYTHIA8, which preserves both the precision of partonic N-jet cross sections in GENEVA as well as the shower accuracy and good description of soft hadronic physics of PYTHIA8. We present results for several underlying-event sensitive observables and compare to data from ATLAS and CMS as well as to standalone PYTHIA8 predictions. This includes a comparison with the recent ATLAS measurement of the beam thrust spectrum, which provides a potential avenue to fully disentangle the physical effects from the primary hard interaction, primary soft radiation, multiple parton interactions, and nonperturbative hadronization.Comment: 23 pages, 11 figures. v3: version accepted by EPJ

A Study of Mathematics Curriculum for Grades 4-6

Teachers are being encouraged to achieve competency in improved mathematics instruction through workshops, in-service training, National Science Foundation Scholarships, Carnegie Foundation Scholarships, and college summer schools. By providing training and sufficient motivation, educators can solve the primary problem in revamping our mathematics\u27 curriculum. But educators are also confronted with the serious challenge of defining and organizing a sound modern curriculum drawn from research on the basic concepts students must have as they move from one level to the next

Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems

Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function is fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more realistic discrete optimization problems. The main challenge is the repeated solving of the optimization problem. To this end, we investigate ways to relax the problem as well as warmstarting the learning and the solving. Our results show that even for discrete problems it often suffices to train by solving the relaxation in the SPO loss. Furthermore, this approach outperforms, for most instances, the state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with weighted knapsack problems as well as complex scheduling problems and show for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems

Constraint Programming for Multi-criteria Conceptual Clustering

International audienceA conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an N P-complete problem for which Constraint Programming (CP) and Integer Linear Programming (ILP) approaches have been recently proposed. We introduce new CP models to solve this problem: a pure CP model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses CP to select a subset of formal concepts that defines a partition. We compare our new models with recent CP and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings

Competition and facilitation between the marine nitrogen-fixing <i>cyanobacterium</i> Cyanothece and its associated bacterial community

N2-fixing cyanobacteria represent a major source of new nitrogen and carbon for marine microbial communities, but little is known about their ecological interactions with associated microbiota. In this study we investigated the interactions between the unicellular N2-fixing cyanobacterium Cyanothece sp. Miami BG043511 and its associated free-living chemotrophic bacteria at different concentrations of nitrate and dissolved organic carbon and different temperatures. High temperature strongly stimulated the growth of Cyanothece, but had less effect on the growth and community composition of the chemotrophic bacteria. Conversely, nitrate and carbon addition did not significantly increase the abundance of Cyanothece, but strongly affected the abundance and species composition of the associated chemotrophic bacteria. In nitrate-free medium the associated bacterial community was co-dominated by the putative diazotroph Mesorhizobium and the putative aerobic anoxygenic phototroph Erythrobacter and after addition of organic carbon also by the Flavobacterium Muricauda. Addition of nitrate shifted the composition toward co-dominance by Erythrobacter and the Gammaproteobacterium Marinobacter. Our results indicate that Cyanothece modified the species composition of its associated bacteria through a combination of competition and facilitation. Furthermore, within the bacterial community, niche differentiation appeared to play an important role, contributing to the coexistence of a variety of different functional groups. An important implication of these findings is that changes in nitrogen and carbon availability due to, e.g., eutrophication and climate change are likely to have a major impact on the species composition of the bacterial community associated with N2-fixing cyanobacteria

An index to quantify an individual's scientific research output that takes into account the effect of multiple coauthorship

I propose the index $\hbar$ ("hbar"), defined as the number of papers of an individual that have citation count larger than or equal to the $\hbar$ of all coauthors of each paper, as a useful index to characterize the scientific output of a researcher that takes into account the effect of multiple coauthorship. The bar is higher for $\hbar$.Comment: A few minor changes from v1. To be published in Scientometric

Effects of Policies Designed to Keep Firearms from High-Risk Individuals

This article summarizes and critiques available evidence from studies published between 1999 and August 2014 on the effects of policies designed to keep firearms from high-risk individuals in the United States. Some prohibitions for high-risk individuals (e.g., those under domestic violence restraining orders, violent misdemeanants) and procedures for checking for more types of prohibiting conditions are associated with lower rates of violence. Certain laws intended to prevent prohibited persons from accessing firearms -- rigorous permit-to-purchase, comprehensive background checks, strong regulation and oversight of gun dealers, and requiring gun owners to promptly report lost or stolen firearms -- are negatively associated with the diversion of guns to criminals. Future research is needed to examine whether these laws curtail nonlethal gun violence and whether the effects of expanding prohibiting conditions for firearm possession are modified by the presence of policies to prevent diversion

Microbial carbon metabolism associated with electrogenic sulphur oxidation in coastal sediments

Recently, a novel electrogenic type of sulphur oxidation was documented in marine sediments, whereby filamentous cable bacteria (Desulfobulbaceae) are mediating electron transport over cm-scale distances. These cable bacteria are capable of developing an extensive network within days, implying a highly efficient carbon acquisition strategy. Presently, the carbon metabolism of cable bacteria is unknown, and hence we adopted a multidisciplinary approach to study the carbon substrate utilization of both cable bacteria and associated microbial community in sediment incubations. Fluorescence in situ hybridization showed rapid downward growth of cable bacteria, concomitant with high rates of electrogenic sulphur oxidation, as quantified by microelectrode profiling. We studied heterotrophy and autotrophy by following 13C-propionate and -bicarbonate incorporation into bacterial fatty acids. This biomarker analysis showed that propionate uptake was limited to fatty acid signatures typical for the genus Desulfobulbus. The nanoscale secondary ion mass spectrometry analysis confirmed heterotrophic rather than autotrophic growth of cable bacteria. Still, high bicarbonate uptake was observed in concert with the development of cable bacteria. Clone libraries of 16S complementary DNA showed numerous sequences associated to chemoautotrophic sulphur-oxidizing Epsilon- and Gammaproteobacteria, whereas 13C-bicarbonate biomarker labelling suggested that these sulphur-oxidizing bacteria were active far below the oxygen penetration. A targeted manipulation experiment demonstrated that chemoautotrophic carbon fixation was tightly linked to the heterotrophic activity of the cable bacteria down to cm depth. Overall, the results suggest that electrogenic sulphur oxidation is performed by a microbial consortium, consisting of chemoorganotrophic cable bacteria and chemolithoautotrophic Epsilon- and Gammaproteobacteria. The metabolic linkage between these two groups is presently unknown and needs further study