A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions

Kavli Affiliate: Jing Wang | First 5 Authors: Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, Susan P Fisher-Hoch, Joseph B Mccormick | Summary: Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of risk factors, […]


Continue.. A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions

On odd number of fermion zero modes on solitons in quantum field theory and string/M theory

Kavli Affiliate: Yuji Tachikawa | First 5 Authors: Yotaro Sato, Yuji Tachikawa, Taizan Watari, , | Summary: We argue that having an odd number of Majorana fermion zero modes on a dynamical point-like soliton signifies an inconsistency in a theory with 3+1 and higher dimensions. We check this statement in a couple of examples in […]


Continue.. On odd number of fermion zero modes on solitons in quantum field theory and string/M theory

Large deviation principle for stochastic heat equation with general rough noise

Kavli Affiliate: Ran Wang | First 5 Authors: Ruinan Li, Ran Wang, Beibei Zhang, , | Summary: We study Freidlin-Wentzell’s large deviation principle for one dimensional nonlinear stochastic heat equation driven by a Gaussian noise: $$frac{partial u^varepsilon(t,x)}{partial t} = frac{partial^2 u^varepsilon(t,x)}{partial x^2}+sqrt{varepsilon} sigma(t, x, u^varepsilon(t,x))dot{W}(t,x),quad t> 0,, xinmathbb{R},$$ where $dot W$ is white in time […]


Continue.. Large deviation principle for stochastic heat equation with general rough noise

A large deviation principle for the stochastic heat equation with general rough noise

Kavli Affiliate: Ran Wang | First 5 Authors: Ruinan Li, Ran Wang, Beibei Zhang, , | Summary: We study Freidlin-Wentzell’s large deviation principle for one dimensional nonlinear stochastic heat equation driven by a Gaussian noise: $$frac{partial u^varepsilon(t,x)}{partial t} = frac{partial^2 u^varepsilon(t,x)}{partial x^2}+sqrt{varepsilon} sigma(t, x, u^varepsilon(t,x))dot{W}(t,x),quad t> 0,, xinmathbb{R},$$ where $dot W$ is white in time […]


Continue.. A large deviation principle for the stochastic heat equation with general rough noise

High-Precision Redshifts for Type Ia Supernovae with the Nancy Grace Roman Space Telescope P127 Prism

Kavli Affiliate: Richard Kessler | First 5 Authors: Bhavin A. Joshi, Louis-Gregory Strolger, Russell E. Ryan, Jr., Alexei V. Filippenko, Rebekah Hounsell | Summary: We present results from simulating slitless spectroscopic observations with the Nancy Grace Roman Space Telescope’s (Roman) Wide-Field Instrument (WFI) P127 prism spanning 0.75 $mu m$ to 1.8 $mu m$. We quantify […]


Continue.. High-Precision Redshifts for Type Ia Supernovae with the Nancy Grace Roman Space Telescope P127 Prism

High-Precision Redshifts for Type Ia Supernovae with the Nancy Grace Roman Space Telescope P127 Prism

Kavli Affiliate: Richard Kessler | First 5 Authors: Bhavin A. Joshi, Louis-Gregory Strolger, Russell E. Ryan, Jr., Alexei V. Filippenko, Rebekah Hounsell | Summary: We present results from simulating slitless spectroscopic observations with the Nancy Grace Roman Space Telescope’s (Roman) Wide-Field Instrument (WFI) P127 prism spanning 0.75 $mu m$ to 1.8 $mu m$. We quantify […]


Continue.. High-Precision Redshifts for Type Ia Supernovae with the Nancy Grace Roman Space Telescope P127 Prism

Removing the fat from your posterior samples with margarine

Kavli Affiliate: George Efstathiou | First 5 Authors: Harry T. J. Bevins, William J. Handley, Pablo Lemos, Peter H. Sims, Eloy de Lera Acedo | Summary: Bayesian workflows often require the introduction of nuisance parameters, yet for core science modelling one needs access to a marginal posterior density. In this work we use masked autoregressive […]


Continue.. Removing the fat from your posterior samples with margarine

Removing the fat from your posterior samples with margarine

Kavli Affiliate: George Efstathiou | First 5 Authors: Harry T. J. Bevins, William J. Handley, Pablo Lemos, Peter H. Sims, Eloy de Lera Acedo | Summary: Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic […]


Continue.. Removing the fat from your posterior samples with margarine

Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators

Kavli Affiliate: George Efstathiou | First 5 Authors: Harry T. J. Bevins, William J. Handley, Pablo Lemos, Peter H. Sims, Eloy de Lera Acedo | Summary: Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic […]


Continue.. Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators

Gravity as a gapless phase and biform symmetries

Kavli Affiliate: Austin Joyce | First 5 Authors: Kurt Hinterbichler, Diego M. Hofman, Austin Joyce, GrĂ©goire Mathys, | Summary: We study effective field theories (EFTs) enjoying (maximal) biform symmetries. These are defined by the presence of a conserved (electric) current that has the symmetries of a Young tableau with two columns of equal length. When […]


Continue.. Gravity as a gapless phase and biform symmetries