scholarly journals RNAxplorer: Harnessing the Power of Guiding Potentials to Sample RNA Landscapes

2020 ◽  
Author(s):  
Gregor Entzian ◽  
Ivo Hofacker ◽  
Yann Ponty ◽  
Ronny Lorenz ◽  
Andrea Tanzer
Keyword(s):  
Adaptive Sampling ◽  
Sampling Methods ◽  
Coarse Graining ◽  
Difficult Problem ◽  
Alternative Structures ◽  
Structure Space ◽  
Folding Dynamics ◽  
Sampling Algorithms ◽  
Kinetic Traps ◽  
Folding Space

AbstractMotivationPredicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space.MethodWe introduceRNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs.RNAxploreruses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling towards underrepresented or unexplored regions of the structure space.ResultsWe developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show thatRNAxplorerproduces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics.Availabilityhttps://github.com/ViennaRNA/RNAxplorer/[email protected],[email protected]

scholarly journals RNAxplorer: harnessing the power of guiding potentials to sample RNA landscapes

2021 ◽  
Author(s):  
Gregor Entzian ◽  
Ivo L Hofacker ◽  
Yann Ponty ◽  
Ronny Lorenz ◽  
Andrea Tanzer
Keyword(s):  
Adaptive Sampling ◽  
Sampling Methods ◽  
Coarse Graining ◽  
Difficult Problem ◽  
Supplementary Information ◽  
Alternative Structures ◽  
Structure Space ◽  
Folding Dynamics ◽  
Sampling Algorithms ◽  
Folding Space

Abstract Motivation Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling toward underrepresented or unexplored regions of the structure space. Results We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics. Availabilityand implementation https://github.com/ViennaRNA/RNAxplorer/. Supplementary information Supplementary data are available at Bioinformatics online.

Adaptive sampling methods for millimeter-wave radiometric imaging based on visual saliency and block information weight

2021 ◽  
Author(s):  
Taiyang Hu ◽  
Jinyu Zhang ◽  
Xiaolang Shao ◽  
Lei He ◽  
Mengxuan Xiao ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Adaptive Sampling ◽  
Sampling Methods ◽  
Visual Saliency

Expected-Improvement-Based Methods for Adaptive Sampling in Multi-Objective Optimization Problems

2016 ◽  
Author(s):  
Jesper Kristensen ◽  
You Ling ◽  
Isaac Asher ◽  
Liping Wang
Keyword(s):  
Convex Hull ◽  
Design Optimization ◽  
Adaptive Sampling ◽  
Pareto Front ◽  
Performance Metrics ◽  
Sampling Methods ◽  
Optimization Problems ◽  
Expected Improvement ◽  
Multi Objective Optimization ◽  
Multi Objective

Adaptive sampling methods have been used to build accurate meta-models across large design spaces from which engineers can explore data trends, investigate optimal designs, study the sensitivity of objectives on the modeling design features, etc. For global design optimization applications, adaptive sampling methods need to be extended to sample more efficiently near the optimal domains of the design space (i.e., the Pareto front/frontier in multi-objective optimization). Expected Improvement (EI) methods have been shown to be efficient to solve design optimization problems using meta-models by incorporating prediction uncertainty. In this paper, a set of state-of-the-art methods (hypervolume EI method and centroid EI method) are presented and implemented for selecting sampling points for multi-objective optimizations. The classical hypervolume EI method uses hyperrectangles to represent the Pareto front, which shows undesirable behavior at the tails of the Pareto front. This issue is addressed utilizing the concepts from physical programming to shape the Pareto front. The modified hypervolume EI method can be extended to increase local Pareto front accuracy in any area identified by an engineer, and this method can be applied to Pareto frontiers of any shape. A novel hypervolume EI method is also developed that does not rely on the assumption of hyperrectangles, but instead assumes the Pareto frontier can be represented by a convex hull. The method exploits fast methods for convex hull construction and numerical integration, and results in a Pareto front shape that is desired in many practical applications. Various performance metrics are defined in order to quantitatively compare and discuss all methods applied to a particular 2D optimization problem from the literature. The modified hypervolume EI methods lead to dramatic resource savings while improving the predictive capabilities near the optimal objective values.

Fast Convergence to Wardrop Equilibria by Adaptive Sampling Methods

2010 ◽  
Vol 39 (8) ◽  
pp. 3700-3735 ◽  
Author(s):  
Simon Fischer ◽  
Harald Räcke ◽  
Berthold Vöcking
Keyword(s):  
Adaptive Sampling ◽  
Sampling Methods ◽  
Fast Convergence

scholarly journals Self-Sampling for Neural Point Cloud Consolidation

2021 ◽  
Vol 40 (5) ◽  
pp. 1-14
Author(s):  
Gal Metzer ◽  
Rana Hanocka ◽  
Raja Giryes ◽  
Daniel Cohen-Or
Keyword(s):  
Point Cloud ◽  
Sampling Methods ◽  
Difficult Problem ◽  
Local Statistics ◽  
Novel Technique ◽  
Input Point ◽  
Shape Learning ◽  
Fixed Set ◽  
Point Set ◽  
Non Local

We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. Unlike other point up-sampling methods which analyze shapes via local patches, in this work, we learn from global subsets. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. Specifically, we define source and target subsets according to the desired consolidation criteria (e.g., generating sharp points or points in sparse regions). The network learns a mapping from source to target subsets, and implicitly learns to consolidate the point cloud. During inference, the network is fed with random subsets of points from the input, which it displaces to synthesize a consolidated point set. We leverage the inductive bias of neural networks to eliminate noise and outliers, a notoriously difficult problem in point cloud consolidation. The shared weights of the network are optimized over the entire shape, learning non-local statistics and exploiting the recurrence of local-scale geometries. Specifically, the network encodes the distribution of the underlying shape surface within a fixed set of local kernels, which results in the best explanation of the underlying shape surface. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.

scholarly journals Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success

2019 ◽  
Author(s):  
Richard Scalzo ◽  
David Kohn ◽  
Hugo Olierook ◽  
Gregory Houseman ◽  
Rohitash Chandra ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Adaptive Sampling ◽  
Sampling Methods ◽  
South Australia ◽  
Monte Carlo Sampling ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Dimensional Parameter

Abstract. The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multimodal; properties of any single mode become inadequate uncertainty measures, and sampling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty, using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has complex local covariance structure, hindering the efficiency of adaptive sampling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank-Nicholson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics or on 3-D geological structure, affect the shape and separation of posterior modes, influencing sampling performance as well as the inversion results. Use of uninformative priors on sensor noise can improve inversion results by enabling optimal weighting among multiple sensors even if noise levels are uncertain. Efficiency could be further increased by using posterior gradient information within proposals, which Obsidian does not currently support, but which could be emulated using posterior surrogates.

scholarly journals An Ensemble of Adaptive Surrogate Models Based on Local Error Expectations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huanwei Xu ◽  
Xin Zhang ◽  
Hao Li ◽  
Ge Xiang
Keyword(s):  
Surrogate Model ◽  
Adaptive Sampling ◽  
Sampling Methods ◽  
Sampling Strategy ◽  
Surrogate Models ◽  
Local Error ◽  
Test Functions ◽  
Application Problem ◽  
Benchmark Test Functions ◽  
Global And Local

An ensemble of surrogate models with high robustness and accuracy can effectively avoid the difficult choice of surrogate model. However, most of the existing ensembles of surrogate models are constructed with static sampling methods. In this paper, we propose an ensemble of adaptive surrogate models by applying adaptive sampling strategy based on expected local errors. In the proposed method, local error expectations of the surrogate models are calculated. Then according to local error expectations, the new sample points are added within the dominating radius of the samples. Constructed by the RBF and Kriging models, the ensemble of adaptive surrogate models is proposed by combining the adaptive sampling strategy. The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.

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