Results 121 to 130 of about 1,412,360 (277)

DYNAMIC PARAMETERS ESTIMATION OF INTERFEROMETRIC SIGNALS BASED ON SEQUENTIAL MONTE CARLO METHOD [PDF]

Научно-технический вестник информационных технологий, механики и оптики, 2014
The paper deals with sequential Monte Carlo method applied to problem of interferometric signals parameters estimation. The method is based on the statistical approximation of the posterior probability density distribution of parameters.
M. A. Volynsky, I. P. Gurov, P. A. Ermolaev, P. S. Skakov   +3 more
doaj  

Iterative Selection of DNA Nanostructures for Cellular Uptake

Advanced Materials, EarlyView.
DNA nanostructures are promising cell targeting delivery vehicles for therapeutics, but the targeting behavior is not fully understood. In this study, libraries of DNA nanostructures that can be amplified and sequenced are combined with cellular uptake as a selection pressure to iteratively refine DNA structures taken up in cells to better understand ...
Anjali Rajwar, Lisa Eichhorn, Jakub Palacka, Sandra Ly, Erik Benson   +4 more
wiley   +1 more source

Probabilistic Cellular Automata Monte Carlo for the Maximum Clique Problem

Mathematics
We consider the problem of finding the largest clique of a graph. This is an NP-hard problem and no exact algorithm to solve it exactly in polynomial time is known to exist.
Alessio Troiani
doaj   +1 more source

Sequential Monte Carlo testing by betting

Journal of the Royal Statistical Society Series B: Statistical Methodology
Abstract In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones.
Lasse Fischer, Aaditya Ramdas
openaire   +2 more sources

Topological Spin‐Texture Transitions in van der Waals Magnets Revealed by X‐Ray Fourier Transform Holography

Advanced Materials, EarlyView.
Applied here for the first time to a 2D material, high‐resolution synchrotron x‐ray Fourier transform holography directly images topological spin textures in Fe3GeTe2. Combined with atomistic tight‐binding simulations including itinerant electrons and Rashba spin‐orbit coupling, the approach reveals labyrinth, skyrmion, and mixed phases, and uncovers ...
Sourav Chowdhury, Soumyaranjan Dash, Michael Schneider, Christopher Klose, Chithra H. Sharma, Lisa‐Marie Kern, Tim A. Butcher, Josefin Fuchs, Santanu Pakhira, Samik DuttaGupta, Takashi Taniguchi, Kenji Watanabe, Sujit Das, Sanjeev Kumar, Bastian Pfau, Amir‐Abbas Haghighirad, Moritz Hoesch   +16 more
wiley   +1 more source

Monte Carlo methods for the estimation of value-at-risk and related risk measures [PDF]

, 2011
Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss.
Marks, Dean
core   +1 more source

Bioinspired Mechanosensitive Organic Artificial Neuron With Programmable Excitatory and Inhibitory Responses via Nonlinear Mechano‐Electrochemical Coupling

Advanced Materials, EarlyView.
Organic artificial neurons couple mechanical deformation and ionic environments through nonlinear mechano‐electrochemical dynamics. Mechanical strain and electrolyte concentration reshape their nonlinear electrical characteristics, programming excitatory or inhibitory spiking responses that mimic mechanosensitive biological neurons and enable ...
Rassen Boukraa, Pietro Belleri, Zsolt M. Kovács‐Vajna, Paschalis Gkoupidenis, Fabrizio Torricelli   +4 more
wiley   +1 more source

Framework Flexibility on Water Sorption and Transport in ZIF‐90: Insights from Neural‐Network Molecular Dynamics

Advanced Materials Interfaces, EarlyView.
A DeepMD‐based molecular dynamics framework is developed to investigate water adsorption in ZIF‐90, explicitly accounting for framework flexibility. Results show that atomistic flexibility significantly influences adsorption energetics, diffusion, and structural correlations, yielding nearly constant heat of adsorption and enhanced mobility.
Nick Mackus, Ionut Tranca, Veerapandian Ponnuchamy, Silvia Gaastra‐Nedea   +3 more
wiley   +1 more source

Global Sampling for Sequential Filtering over Discrete State Space

EURASIP Journal on Advances in Signal Processing, 2004
In many situations, there is a need to approximate a sequence of probability measures over a growing product of finite spaces. Whereas it is in general possible to determine analytic expressions for these probability measures, the number of computations
Cheung-Mon-Chan Pascal, Moulines Eric
doaj   +1 more source

Convergence of the SMC implementation of the PHD filter [PDF]

, 2005
The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem.
Singh, Sumeetpal S. (Sumeetpal Sidhu), Vo, Ba-Ngu, Johansen, Adam M., Singh, SS, Doucet, Arnaud, Doucet, A, Vo, B, Vo, B-N, ,, Johansen, AM   +9 more
core   +1 more source