 Editors:
 Doglioni, C.; Kim, D.; Stewart, G.A.; Silvestris, L.; Jackson, P.; Kamleh, W.
 Award ID(s):
 1836650
 Publication Date:
 NSFPAR ID:
 10256982
 Journal Name:
 EPJ Web of Conferences
 Volume:
 245
 Page Range or eLocationID:
 06026
 ISSN:
 2100014X
 Sponsoring Org:
 National Science Foundation
More Like this

Abernethy, Jacob ; Agarwal, Shivani (Ed.)We study a variant of the sparse PCA (principal component analysis) problem in the “hard” regime, where the inference task is possible yet no polynomialtime algorithm is known to exist. Prior work, based on the lowdegree likelihood ratio, has conjectured a precise expression for the best possible (subexponential) runtime throughout the hard regime. Following instead a statistical physics inspired point of view, we show bounds on the depth of free energy wells for various Gibbs measures naturally associated to the problem. These free energy wells imply hitting time lower bounds that corroborate the lowdegree conjecture: we show that a classmore »

A bstract One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameterspace degeneracies. An important example is measuring the topquark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the topquark mass parameter. We compare four different methodologies for topquark mass measurement: a classical histogram fit similar to one commonly used in experiment augmentedmore »

Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observedmore »

Abstract The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layerbylayer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theorybased approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metalmore »

ABSTRACT We present cosmological parameter constraints based on a joint modelling of galaxy–lensing crosscorrelations and galaxy clustering measurements in the SDSS, marginalizing over smallscale modelling uncertainties using mock galaxy catalogues, without explicit modelling of galaxy bias. We show that our modelling method is robust to the impact of different choices for how galaxies occupy dark matter haloes and to the impact of baryonic physics (at the $\sim 2{{\ \rm per\ cent}}$ level in cosmological parameters) and test for the impact of covariance on the likelihood analysis and of the survey window function on the theory computations. Applying our results tomore »