Precise determination of the lattice spacing in full lattice QCD

We compare three different methods to determine the lattice spacing in lattice QCD and give results from calculations on the MILC ensembles of configurations that include the effect of $u$, $d$ and $s$ sea quarks. It is useful, for ensemble to ensemble comparison, to express the results as giving a physical value for $r_1$, a parameter from the heavy quark potential. Combining the three methods gives a value for $r_1$ in the continuum limit of 0.3133(23)(3) fm. Using the MILC values for $r_0/r_1$, this corresponds to a value for the $r_0$ parameter of 0.4661(38) fm. We also discuss how to use the $\eta_s$ for determining the lattice spacing and tuning the $s$-quark mass accurately, by giving values for $m_{\eta_s}$ (0.6858(40) GeV) and $f_{\eta_s}$ (0.1815(10) GeV).Comment: 15 page

A Structured Framework and Resources to Use to Get Your Medical Education Work Published.

IntroductionMedical educators often have great ideas for medical education scholarship but have difficulty converting their educational abstract or project into a published manuscript.MethodsDuring this workshop, participants addressed common challenges in developing an educational manuscript. In small-group case scenarios, participants discovered the importance of the "So what?" in making the case for their project. Incorporating conceptual frameworks, participants chose appropriate outcome metrics, discussed how to frame the discussion section, and ensured appropriate journal fit. After each small-group exercise, large-group discussions allowed the small groups to report back so that facilitators could highlight and reinforce key learning points. At the conclusion of the workshop, participants left with a checklist for creating an educational manuscript and an additional resources document to assist them in avoiding common pitfalls when turning their educational abstract/project into a publishable manuscript.ResultsThis workshop was presented in 2016 and 2017. Presenter evaluations were completed by 33 participants; 11 completed conference evaluations. The mean overall rating on presenter evaluations was 4.55 out of 5, while the conference evaluations mean was 3.73 out of 4. Comments provided on both evaluation tools highlighted the perceived effectiveness of the delivery and content. More than 50% of respondents stated that they planned to incorporate the use of conceptual frameworks in future work.DiscussionThis workshop helped participants address common challenges by providing opportunities for hands-on practice as well as tips and resources for use when submitting a medical education manuscript for publication

An explanation of the Newman-Janis Algorithm

After the original discovery of the Kerr metric, Newman and Janis showed that this solution could be ``derived'' by making an elementary complex transformation to the Schwarzschild solution. The same method was then used to obtain a new stationary axisymmetric solution to Einstein's field equations now known as the Kerr-newman metric, representing a rotating massive charged black hole. However no clear reason has ever been given as to why the Newman-Janis algorithm works, many physicist considering it to be an ad hoc procedure or ``fluke'' and not worthy of further investigation. Contrary to this belief this paper shows why the Newman-Janis algorithm is successful in obtaining the Kerr-Newman metric by removing some of the ambiguities present in the original derivation. Finally we show that the only perfect fluid generated by the Newman-Janis algorithm is the (vacuum) Kerr metric and that the only Petrov typed D solution to the Einstein-Maxwell equations is the Kerr-Newman metric.Comment: 14 pages, no figures, submitted to Class. Quantum Gra

Tuning Monte Carlo Generators: The Perugia Tunes

We present 9 new tunes of the pT-ordered shower and underlying-event model in PYTHIA 6.4. These "Perugia" tunes update and supersede the older "S0" family. The data sets used to constrain the models include hadronic Z0 decays at LEP, Tevatron minimum-bias data at 630, 1800, and 1960 GeV, Tevatron Drell-Yan data at 1800 and 1960 GeV, and SPS min-bias data at 200, 546, and 900 GeV. In addition to the central parameter set, called "Perugia 0", we introduce a set of 8 related "Perugia Variations" that attempt to systematically explore soft, hard, parton density, and colour structure variations in the theoretical parameters. Based on these variations, a best-guess prediction of the charged track multiplicity in inelastic, nondiffractive minimum-bias events at the LHC is made. Note that these tunes can only be used with PYTHIA 6, not with PYTHIA 8. Note: this report was updated in March 2011 with a new set of variations, collectively labeled "Perugia 2011", that are optimized for matching applications and which also take into account some lessons from the early LHC data. In order not to break the original text, these are described separately in Appendix B. Note 2: a subsequent "Perugia 2012" update is described in Appendix C.Comment: 46 page

Dynamic structure factor of Luttinger liquids with quadratic energy dispersion and long-range interactions

We calculate the dynamic structure factor S (omega, q) of spinless fermions in one dimension with quadratic energy dispersion k^2/2m and long range density-density interaction whose Fourier transform f_q is dominated by small momentum-transfers q << q_0 << k_F. Here q_0 is a momentum-transfer cutoff and k_F is the Fermi momentum. Using functional bosonization and the known properties of symmetrized closed fermion loops, we obtain an expansion of the inverse irreducible polarization to second order in the small parameter q_0 / k_F. In contrast to perturbation theory based on conventional bosonization, our functional bosonization approach is not plagued by mass-shell singularities. For interactions which can be expanded as f_q = f_0 + f_0^{2} q^2/2 + O (q^4) with finite f_0^{2} we show that the momentum scale q_c = 1/ | m f_0^{2} | separates two regimes characterized by a different q-dependence of the width gamma_q of the collective zero sound mode and other features of S (omega, q). For q_c << q << k_F we find that the line-shape in this regime is non-Lorentzian with an overall width gamma_q of order q^3/(m q_c) and a threshold singularity at the lower edge.Comment: 33 Revtex pages, 17 figure

Habitat Selection of Northern Bobwhite Coveys on Two Intensive Agriculture Landscapes in Eastern North Carolina (Poster Abstract)

Little information is available for home range size and habitat use of northern bobwhites (Colinus virginianus) on modern agricultural landscapes in autumn. Therefore, we monitored radiomarked bobwhite coveys from September–December 1998 on farms in Wilson and Tyrrell counties, North Carolina. The Tyrrell County farm was a 6084-ha area recently developed for commercial production of corn and soybeans. Dispersed throughout crop areas were forested and fallow blocks at differing stages of succession. The Wilson County farms had small fields (x̄ = 1.8 ha, SE = 0.12) planted in cotton, soybeans, corn, and tobacco and were surrounded by mixed pine and hardwood blocks of differing ages. Mean home range size at the Tyrrell County farm was 33.2 ha (range 4.5–128.5 ha) (n = 10). The two largest home ranges, 70.7 and 128.6 ha, were disproportionately large due to large movements from harvested crop fields to permanent forested cover. Covey home ranges were not established at random (l = 0.124; x2 4= 20.18; P \u3c 0.001). Road and canal edges were selected significantly more than any other habitat followed in rank by soybean fields, corn fields, forested, and fallow blocks. Road and canal edges provided necessary cover for moving between habitat types, especially from forested and fallow blocks to crop fields. Within home ranges, coveys did not allocate their time at random (l = 0.336; x2 4 = 10.89; P \u3c 0.05). Habitats were ranked in the order of forested blocks, fallow areas, soybean fields, road and canal edges, and corn fields, but no significant differences were found between habitats. In Wilson County, average covey home range was 17.4 ha (Range: 4.9–37.6 ha)(n = 11). Coveys did not establish their home range at random (l = 0.407; x22 = 9.87; P \u3c 0.05), selecting forested blocks over crop fields (T9 = 3.02, P \u3c 0.012). Within home ranges coveys did not allocate their time at random (l = 0.1319; x25 = 22.28; P \u3c 0.001), utilizing primarily forested blocks followed by cotton fields, soybean fields, corn fields, and other areas. On both study areas, forested and fallow blocks were the only source of cover to spend time in after crop harvest. Covey use within forested and fallow blocks was concentrated along edges of crop fields, leaving large portions of this habitat type unused. Forested and fallow blocks were primarily used as loafing cover in between feeding periods in adjacent crop fields

Factors Influencing Early Morning Covey Calling in Northern Bobwhites (Oral Abstract)

Data from early morning covey calling may be useful for measuring abundance of northern bobwhite (Colinus virginianus). However, critical assumptions about detection rates, survey timing, and seasonality effects have not been tested. Additionally, the effects of weather and covey density on call rates are unknown. We quantified call rates of 219 radiomarked coveys at 5 sites in 1998 and 2 sites in 1997 and 1999 to monitor calling behavior of bobwhite coveys. First calls for coveys (n = 442) occurred on average 23.4 (SE = 0.5) min before sunrise and averaged 31.4 +- 1.9 calls/covey. Few first calls (13%) occurred after 15 min before sunrise. Across sites, call rates averaged 58% (SE 2.0) (n = 763). Call rates were most variable during September and December biweekly periods and least variable during late October and early November biweekly periods. We developed 15 logistic regression models from data collected in 1998 for predicting the probability of a covey to call. Selected best models were chosen using the Akaike information criterion modified for overdispersion and small sample size. The selected best model included number of adjacent calling coveys, wind speed, cloud cover, and barometric pressure change. Parameter estimates for number of adjacent calling coveys had an odds ratio of 1.4; the 95% CI did not contain 0. A less parsimonious model, which also included biweekly period and interaction terms, was equally as likely (QAICc 0.32) as the selected model. The 16–31 October biweekly period had an odds ratio of 1.8; conditional 95% CI not containing 0. A post hoc analysis was conducted using the same candidate model list, but we replaced number of adjacent calling coveys with deviations of the number of adjacent calling coveys from site means. Results were similar to the previous analysis with the same selected best model, but model fit was improved. Selected best models were tested using observations collected in 1999 from 2 of the 5 sites monitored in 1998. Predicted call rates were relatively precise (observed call rate-predicted call rate \u3c 0.10) for biweekly periods associated with peak call rates, but call rates were less precise (range 0.12–0.27) for other biweekly periods. Constancy of call rates suggests that at bobwhite densities we observed (0.75 and 5 bobwhites/ha), covey call surveys have potential to index fall populations of bobwhites with reasonable accuracy

Role of social environment and social clustering in spread of opinions in co-evolving networks

Taking a pragmatic approach to the processes involved in the phenomena of collective opinion formation, we investigate two specific modifications to the co-evolving network voter model of opinion formation, studied by Holme and Newman [1]. First, we replace the rewiring probability parameter by a distribution of probability of accepting or rejecting opinions between individuals, accounting for the asymmetric influences in relationships among individuals in a social group. Second, we modify the rewiring step by a path-length-based preference for rewiring that reinforces local clustering. We have investigated the influences of these modifications on the outcomes of the simulations of this model. We found that varying the shape of the distribution of probability of accepting or rejecting opinions can lead to the emergence of two qualitatively distinct final states, one having several isolated connected components each in internal consensus leading to the existence of diverse set of opinions and the other having one single dominant connected component with each node within it having the same opinion. Furthermore, and more importantly, we found that the initial clustering in network can also induce similar transitions. Our investigation also brings forward that these transitions are governed by a weak and complex dependence on system size. We found that the networks in the final states of the model have rich structural properties including the small world property for some parameter regimes. [1] P. Holme and M. Newman, Phys. Rev. E 74, 056108 (2006)

Flare Forecasting Using the Evolution of McIntosh Sunspot Classifications

Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent $\geqslant$C1.0 and $\geqslant$M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996 to December 2008 (SC23), with performance studied by verification metrics. Major findings are: i) considering Brier skill score (BSS) for $\geqslant$C1.0 flares, the evolution-dependent McIntosh-Poisson method ($\text{BSS}_{\text{evolution}}=0.09$) performs better than the static McIntosh-Poisson method ($\text{BSS}_{\text{static}} = -0.09$); ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of $\geqslant$C1.0 rates in SC23 being on average $\approx$80% of those in SC22 (with $\geqslant$M1.0 being $\approx$50%); iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods ($\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{static}} = 0.09$ and $\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{evolution}} = 0.20$) and improved forecast reliability diagrams.Comment: 21 pages, 9 figure