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Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is…

Quantitative Methods · Quantitative Biology 2018-07-27 Diana Barac , Michael D. Multerer , Dagmar Iber

This paper addresses the problem of separating spectral sources which are linearly mixed with unknown proportions. The main difficulty of the problem is to ensure the full additivity (sum-to-one) of the mixing coefficients and…

Methodology · Statistics 2010-08-30 Nicolas Dobigeon , Said Moussaoui , Jean-Yves Tourneret , Cedric Carteret

We describe an algorithm to generate temperature and polarization maps of the cosmic microwave background radiation containing non-Gaussianity of arbitrary local type. We apply an optimized quadrature scheme that allows us to predict and…

Cosmology and Nongalactic Astrophysics · Physics 2024-06-04 Franz Elsner , Benjamin D. Wandelt

In this work, we introduce a generative approach for pose-free (without camera parameters) reconstruction of 360 scenes from a sparse set of 2D images. Pose-free scene reconstruction from incomplete, pose-free observations is usually…

Computer Vision and Pattern Recognition · Computer Science 2025-10-13 Soumava Paul , Prakhar Kaushik , Alan Yuille

Three-dimensional cardiovascular fluid dynamics simulations typically require computation of several cardiac cycles before they reach a periodic solution, rendering them computationally expensive. Furthermore, there is currently no…

Computational Engineering, Finance, and Science · Computer Science 2021-06-28 Martin R. Pfaller , Jonathan Pham , Nathan M. Wilson , David W. Parker , Alison L. Marsden

We propose two methods for generating non-Gaussian maps with fixed power spectrum and bispectrum. The first makes use of a recently proposed rigorous, non-perturbative, Bayesian framework for generating non-Gaussian distributions. The…

Astrophysics · Physics 2009-11-06 Carlo R. Contaldi , Joao Magueijo

Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are…

Methodology · Statistics 2022-12-12 Siqi Liang , Faming Liang

Continuum solvation models are becoming increasingly relevant in condensed matter simulations, allowing to characterize materials interfaces in the presence of wet electrified environments at a reduced computational cost with respect to all…

Computational Physics · Physics 2023-08-16 Gabriel Medrano , Edan Bainglass , Oliviero Andreussi

We consider Gaussian quantum circuits supplemented with non-Gaussian input states and derive sufficient conditions for efficient classical strong simulation of these circuits. In particular, we generalise the stellar representation of…

Quantum Physics · Physics 2021-07-14 Ulysse Chabaud , Giulia Ferrini , Frédéric Grosshans , Damian Markham

We present a generic algorithm for generating Gaussian random initial conditions for cosmological simulations on periodic rectangular lattices. We show that imposing periodic boundary conditions on the real-space correlator and choosing…

Astrophysics · Physics 2009-10-30 Ue-Li Pen

We revisit a possible scale-dependence of local-type primordial non-Gaussianities induced by super-horizon evolution of scalar field perturbations. We develop the formulation based on $\delta N$ formalism and derive the generalized form of…

Cosmology and Nongalactic Astrophysics · Physics 2021-12-22 Daisuke Yamauchi , Shuichiro Yokoyama , Tomo Takahashi

We present an algorithm for quickly generating multiple realizations of N-body simulations to be used, for example, for cosmological parameter estimation from surveys of large-scale structure. Our algorithm uses a new method to resample the…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-27 Michael D. Schneider , Shaun Cole , Carlos S. Frenk , Istvan Szapudi

We address realistic schemes for the generation of non-Gaussian states of light based on conditional intensity measurements performed on correlated bipartite states. We consider both quantum and classically correlated states and different…

We provide a graphical method to describe and analyze non-Gaussian quantum states using a hypergraph framework. These states are pivotal resources for quantum computing, communication, and metrology, but their characterization is hindered…

Quantum Physics · Physics 2025-07-28 Lina Vandré , Boxuan Jing , Yu Xiang , Otfried Gühne , Qiongyi He

Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the…

We discuss how primordial non-Gaussianity of the curvature perturbation helps to constrain models of the early universe. Observations are consistent with Gaussian initial conditions, compatible with the predictions of the simplest models of…

Cosmology and Nongalactic Astrophysics · Physics 2014-11-26 Christian T. Byrnes

Cosmological N-body simulations aim to calculate the non-linear gravitational growth of structures via particle dynamics. A crucial problem concerns the setting-up of the initial particle distribution, as standard theories of galaxy…

Astrophysics · Physics 2010-12-09 Thierry Baertschiger , Francesco Sylos Labini

A detection or nondetection of primordial non-Gaussianity by using the cosmic microwave background radiation (CMB) offers a way of discriminating inflationary scenarios and testing alternative models of the early universe. This has…

Cosmology and Nongalactic Astrophysics · Physics 2012-02-15 A. Bernui , M. J. Reboucas , A. F. F. Teixeira

We investigate non-Gaussian states of light as ancillary inputs for generating nonlinear transformations required for quantum computing with continuous variables. We consider a recent proposal for preparing a cubic phase state, find the…

Quantum Physics · Physics 2009-11-13 Shohini Ghose , Barry C. Sanders

In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mainly laid on sequential Monte Carlo methods, which are based…

Methodology · Statistics 2023-04-28 Konstantinos E. Tatsis , Vasilis K. Dertimanis , Eleni N. Chatzi
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