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Deep convolutional reconstruction for gradient-domain rendering

Markus Kettunen, Erik Härkönen, Jaakko Lehtinen
2019 ACM Transactions on Graphics  
Our results significantly improve the quality obtained from gradientdomain path tracing, allowing it to overtake state-of-the-art comparison techniques that denoise traditional Monte Carlo samplings.  ...  However, while they often yield state of the art performance among algorithms that are based on Monte Carlo sampling alone, gradient-domain rendering algorithms have, until now, not generally been competitive  ...  for converting many of them to Mitsuba.  ... 
doi:10.1145/3306346.3323038 fatcat:5d3alsh4hneqzgoiasg6myyldi

Dynamic Diffuse Global Illumination Resampling [article]

Zander Majercik, Thomas Müller, Alexander Keller, Derek Nowrouzezahrai, Morgan McGuire
2021 arXiv   pre-print
Moreover, when combined with commodity denoisers, we are able to interactively render global illumination in complex scenes.  ...  Specialized sampling strategies are effective for specular and near-specular transport because the scattering has relatively low directional variance per scattering event.  ...  “Nonlinearly Weighted First-Order Regression for De- BACHER , C ARSTEN . “Multiplexed Metropolis Light Transport”. ACM noising Monte Carlo Renderings”. Comput. Graph.  ... 
arXiv:2108.05263v1 fatcat:uhy2bwix3bekzamr7pscqxoeia

Neural Radiosity [article]

Saeed Hadadan, Shuhong Chen, Matthias Zwicker
2021 arXiv   pre-print
Our approach decouples solving the rendering equation from rendering (perspective) images similar as in traditional radiosity techniques, and allows us to efficiently synthesize arbitrary views of a scene  ...  We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing the norm of its residual similar as in traditional radiosity techniques.  ...  Monte Carlo Estimate.  ... 
arXiv:2105.12319v1 fatcat:6nr62aopaff6tdtbmhw3lp36vm

The Integration of Functional Brain Activity from Adolescence to Adulthood

Prantik Kundu, Brenda E. Benson, Dana Rosen, Sophia Frangou, Ellen Leibenluft, Wen-Ming Luh, Peter A. Bandettini, Daniel S. Pine, Monique Ernst
2018 Journal of Neuroscience  
ME-fMRI acquires blood oxygenation level-dependent (BOLD) signals while also quantifying susceptibility-weighted transverse relaxation time (T 2 *) signal decay.  ...  Functional MRI (fMRI) is key for characterizing changes in brain function that accompany development.  ...  The resulting parametric map was then cluster corrected, based on Monte Carlo ␣ probability simulations, to ␣ Ͻ 0.05.  ... 
doi:10.1523/jneurosci.1864-17.2018 pmid:29487126 pmcid:PMC5895042 fatcat:cigelbxmuzexvdhmkhtvmpmba4

Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

Cristina Mollica, Lea Petrella
2016 Journal of Applied Statistics  
The teams act autonomously within the framework of the WG in order to promote their own research agenda. Their activities are endorsed by the WG.  ...  Specialized teams Currently the ERCIM WG has over 1150 members and the following specialized teams BM: Bayesian Methodology CODA: Complex data structures and Object Data Analysis CPEP: Component-based methods for  ...  The estimation relies on a Markov-Chain Monte Carlo approach involving a Sequential Monte Carlo algorithm, the Particle Gibbs.  ... 
doi:10.1080/02664763.2016.1263835 fatcat:l5eyielgxrct7hq5ljqeej5ccy

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
In a first step, the absorbed radiation dose in each image voxel is estimated based on a Monte-Carlo simulation of X-ray absorption and scattering.  ...  We performed a simulation study using a Monte Carlo Simulation with primary modulator for DEDR system.  ...  Studies have shown that there is variation in the agreement between operators viewing the same tissue [1] suggesting that a complimentary technique for verification could improve the robustness of the  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models

Michael C. Burkhart, David M. Brandman, Brian Franco, Leigh R. Hochberg, Matthew T. Harrison
2020 Neural Computation  
When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for [Formula: see text] that  ...  We argue that in many cases, a model for [Formula: see text] proves both easier to learn and more accurate for latent state estimation.  ...  Crites for their thoughtful feedback on the manuscript; B. Travers and D. Rosler for administrative support; and C. Grant for clinical assistance.  ... 
doi:10.1162/neco_a_01275 pmid:32187000 fatcat:xqok3xat3bbglmtg7d2sno735y

Diabetes: Models, Signals and control

C Cobelli
2010 Journal of Physics, Conference Series  
trials in the quest for optimal diabetes control.  ...  We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal  ...  a trend component, that describes long-term variations, by a Bayesian method implemented by Monte Carlo Markov chains.  ... 
doi:10.1088/1742-6596/238/1/012003 fatcat:qztkq6ec2fgipecz5j6hdogm7m

Diabetes: Models, Signals, and Control

C. Cobelli, C. Dalla Man, G. Sparacino, L. Magni, G. De Nicolao, B.P. Kovatchev
2009 IEEE Reviews in Biomedical Engineering  
trials in the quest for optimal diabetes control.  ...  We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal  ...  a trend component, that describes long-term variations, by a Bayesian method implemented by Monte Carlo Markov chains.  ... 
doi:10.1109/rbme.2009.2036073 pmid:20936056 pmcid:PMC2951686 fatcat:odoyz3bmardblfvaqplvzesmei

Optoacoustic Imaging and Tomography: Reconstruction Approaches and Outstanding Challenges in Image Performance and Quantification

Christian Lutzweiler, Daniel Razansky
2013 Sensors  
reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers.  ...  Yet, living objects present a complex target for optoacoustic imaging due to the presence of a highly heterogeneous tissue background in the form of strong spatial variations of scattering and absorption  ...  One frequently applied approach to model the transport of diffuse light is the Monte Carlo (MC) method, which simulates a random walk of a great number of photons through the scattering tissue [127] [  ... 
doi:10.3390/s130607345 pmid:23736854 pmcid:PMC3715274 fatcat:uigdgbyowjaxdkl7ev5kuxutfm

Transformation-invariant clustering using the EM algorithm

B.J. Frey, N. Jojic
2003 IEEE Transactions on Pattern Analysis and Machine Intelligence  
For example, if images from a video sequence of a person walking across a cluttered background are clustered, it would be more useful for the different clusters to represent different poses and expressions  ...  We compare this technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images from videos of faces into different categories of identification  ...  as well as Tom Minka for pointing out a useful trick for simplifying determinants of sums of products of matrices.  ... 
doi:10.1109/tpami.2003.1159942 fatcat:hdt2ng22qjbf3bxewlegdgnwmq

Design of large polyphase filters in the Quadratic Residue Number System

Gian Carlo Cardarilli, Alberto Nannarelli, Yann Oster, Massimo Petricca, Marco Re
2010 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers  
We use Monte Carlo numerical examples to demonstrate the advantages of the coupling effect.  ...  We first propose a new version of the SGSD method for computing a third-order CP decomposition.  ... 
doi:10.1109/acssc.2010.5757589 fatcat:ccxnu5owr5fyrcjcqukumerueq

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment [article]

Abhilash Mathews
2022 arXiv   pre-print
measurements of HeI line radiation into local plasma fluctuations is demonstrated via a newly created deep learning framework that integrates neutral transport physics and collisional radiative theory for  ...  With this technique, the first direct quantitative comparisons of turbulent fields between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling are demonstrated with good overall agreement  ...  This permits the self-consistent learning of time-dependent 2-dimensional profiles for neutral species such as atomic and molecular deuterium [30] via application of existing Monte Carlo transport codes  ... 
arXiv:2205.07838v1 fatcat:gzmv7zmx7ne4lixeamiwrtrjhi

Numerical Stabilization of the Melt Front for Laser Beam Cutting [chapter]

Torsten Adolph, Willi Schönauer, Markus Niessen, Wolfgang Schulz
2010 Numerical Mathematics and Advanced Applications 2009  
It is well known that high order numerical schemes exhibit oscillations around shocks but are very efficient for smooth solutions.  ...  We develop a hybrid scheme consisting of a combination of a second order MUSCL scheme and a high order finite difference scheme.  ...  Being a Monte Carlo method, it is computationally intensive due to the slow convergence but well suited for parallelization.  ... 
doi:10.1007/978-3-642-11795-4_6 fatcat:nx4nvuxaxfbcdjknopny53ck5e

Hippocampal and prefrontal theta-band mechanisms underpin implicit spatial context learning

Eelke Spaak, Floris P. de Lange
2019 Journal of Neuroscience  
Furthermore, we demonstrate that one brain mechanism (hippocampal theta-band activity) is responsible for learning in these settings, whereas another mechanism (prefrontal theta-band activity) is involved  ...  the model parameters given the data were obtained per participant using Markov Chain Monte Carlo (MCMC) with a Metropolis-Hastings sampling scheme.  ...  subsequent recognition performance for that display through logistic regression.  ... 
doi:10.1523/jneurosci.1660-19.2019 pmid:31699887 pmcid:PMC6939492 fatcat:qdfdldk4rbgmbdb4oln5xaxmkm
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