49 research outputs found
Superconformal theories from Pseudoparticle Mechanics
We consider a one-dimensional Osp() pseudoparticle mechanical model
which may be written as a phase space gauge theory. We show how the
pseudoparticle model naturally encodes and explains the two-dimensional zero
curvature approach to finding extended conformal symmetries. We describe a
procedure of partial gauge fixing of these theories which leads generally to
theories with superconformally extended -algebras. The pseudoparticle
model allows one to derive the finite transformations of the gauge and matter
fields occurring in these theories with extended conformal symmetries. In
particular, the partial gauge fixing of the Osp() pseudoparticle
mechanical models results in theories with the SO() invariant -extended
superconformal symmetry algebra of Bershadsky and Knizhnik. These algebras are
nonlinear for We discuss in detail the cases of and
giving two new derivations of the superschwarzian derivatives. Some comments
are made in the case on how twisted and topological theories represent a
significant deformation of the original particle model. The particle model also
allows one to interpret superconformal transformations as deformations of flags
in super jet bundles over the associated super Riemann surface.Comment: 36 pages, UTTG-93-00
Field diffeomorphisms and the algebraic structure of perturbative expansions
We consider field diffeomorphisms in the context of real scalar field
theories. Starting from free field theories we apply non-linear field
diffeomorphisms to the fields and study the perturbative expansion for the
transformed theories. We find that tree level amplitudes for the transformed
fields must satisfy BCFW type recursion relations for the S-matrix to remain
trivial. For the massless field theory these relations continue to hold in loop
computations. In the massive field theory the situation is more subtle. A
necessary condition for the Feynman rules to respect the maximal ideal and
co-ideal defined by the core Hopf algebra of the transformed theory is that
upon renormalization all massive tadpole integrals (defined as all integrals
independent of the kinematics of external momenta) are mapped to zero.Comment: 8 pages, 2 figure
Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics.
Correction for âAn open challenge to advance probabilistic forecasting for dengue epidemics,â by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel GarcĂa-DĂez, Marilia SĂĄ Carvalho, Xavier RodĂł, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-GarcĂa, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier RodĂł should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected
An open challenge to advance probabilistic forecasting for dengue epidemics
This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability:
Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue
An open challenge to advance probabilistic forecasting for dengue epidemics.
A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue