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An Accelerated Online PCA with O(1) Complexity for Learning Molecular Dynamics Data [article]

Salaheddin Alakkari, John Dingliana
2018 Workshop on Molecular Graphics and Visual Analysis of Molecular Data  
Since applying standard PCA for such large data is expensive in terms of space and time complexity, we propose a novel online PCA algorithm with O(1) complexity per new timestep.  ...  In this paper, we discuss the problem of decomposing complex and large Molecular Dynamics trajectory data into simple low-resolution representation using Principal Component Analysis (PCA).  ...  Acknowledgements This research has been conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 13/IA/1895.  ... 
doi:10.2312/molva.20181100 dblp:conf/molva-ws/AlakkariD18 fatcat:cgs3e55mbbacnjoxk3zyr7wbay

An Acceleration Scheme for Memory Limited, Streaming PCA [article]

Salaheddin Alakkari, John Dingliana
2018 arXiv   pre-print
In this paper, we propose an acceleration scheme for online memory-limited PCA methods. Our scheme converges to the first k>1 eigenvectors in a single data pass.  ...  Specifically, we discuss a family of time-varying systems that are based on Molecular Dynamics simulations where batch PCA converges to the actual analytic solution of such systems.  ...  Acknowledgements This research has been conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 13/IA/1895.  ... 
arXiv:1807.06530v1 fatcat:z3uaeoim5zadze4dbhcghdmzjy

MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

Robert T. McGibbon, Kyle A. Beauchamp, Matthew P. Harrigan, Christoph Klein, Jason M. Swails, Carlos X. Hernández, Christian R. Schwantes, Lee-Ping Wang, Thomas J. Lane, Vijay S. Pande
2015 Biophysical Journal  
As molecular dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomolecular systems, the necessity of flexible and easy-to-use software tools for the analysis  ...  MDTraj is a powerful and user-friendly software package that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.  ...  To see this figure in color, go online. FIGURE 3 3 Demonstration of PCA with MDTraj, scikit-learn, and MATPLOTLIB. To see this figure in color, go online.  ... 
doi:10.1016/j.bpj.2015.08.015 pmid:26488642 pmcid:PMC4623899 fatcat:dxvukd7e4zhknirknrrz3lxt3i

Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems

Phaedon-Stelios Koutsourelakis, Elias Bilionis
2011 Multiscale Modeling & simulation  
We discuss parallelizable, online inference and learning algorithms that employ sequential Monte Carlo samplers and scale linearly with the dimensionality of the observed dynamics.  ...  Section 2.3 is devoted to inference and learning tasks which involve a locally optimal SMC sampler and an online expectation-maximization scheme.  ...  When dealing with high-dimensional molecular ensembles, for example, each of these building blocks might be an (overdamped) Langevin equation with a harmonic potential [19, 84, 87] .  ... 
doi:10.1137/100783790 fatcat:r7mfzx4sqvdfrktarxt3ak4e7e

State of the Art and Future Trends in Data Reduction for High-Performance Computing

2020 Supercomputing Frontiers and Innovations  
An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments.  ...  In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction.  ...  The computational complexity is O(n 3 ) for Kernel PCA, Isomap and DM, and O((nk) 3 ) for MVU with k nearest neighbors.  ... 
doi:10.14529/jsfi200101 fatcat:rcaotdomv5frfpjnmrf2giubla

Subspace Graph Physics: Real-Time Rigid Body-Driven Granular Flow Simulation [article]

Amin Haeri, Krzysztof Skonieczny
2021 arXiv   pre-print
A graph network simulator (GNS) is trained to learn the underlying subspace dynamics. The learned GNS is then able to predict particle positions and interaction forces with good accuracy.  ...  Principal component analysis (PCA) is used to reduce the dimensionality of data. We show that the first few principal components of our high-dimensional data keep almost the entire variance in data.  ...  Decoder The Decoder ∶ 𝐺(1) → 𝑜̂ 𝑡+1 extracts the dynamics information 𝑜̂ 𝑡+1 from the updated latent graph 𝐺(1) .  ... 
arXiv:2111.10206v1 fatcat:vntwi2zoevfsrfmvohvydhqqte

SpinSPJ: a novel NMR scripting system to implement artificial intelligence and advanced applications

Zao Liu, Zhiwei Chen, Kan Song
2021 BMC Bioinformatics  
NMR researchers can easily call functions of instrument control and data processing as well as developing complex functionality (such as multivariate statistical analysis, deep learning, etc.) with CPython  ...  Background Software for nuclear magnetic resonance (NMR) spectrometers offer general functionality of instrument control and data processing; these applications are often developed with non-scripting languages  ...  Qingyuan Li for their patient programming assistance. We sincerely appreciate the fruitful discussions with Prof. Xiaobo Qu, Prof. Xianzhong Yan, and Mr. Shigan Chai.  ... 
doi:10.1186/s12859-021-04492-y pmid:34875998 pmcid:PMC8650269 fatcat:rxmeuwvb3vg3dmjdjavc3rx7dm

Adaptive Latent Space Tuning for Non-Stationary Distributions [article]

Alexander Scheinker, Frederick Cropp, Sergio Paiagua, Daniele Filippetto
2021 arXiv   pre-print
Powerful deep learning tools, such as convolutional neural networks (CNN), are able to learn the input-output relationships of large complicated systems directly from data.  ...  One important challenge faced by deep learning methods is large non-stationary systems whose characteristics change quickly with time for which re-training is not feasible.  ...  used for general machine learning problems where limited data available for making predictions after a network has been trained to learn underlying relationships and for problems with time-varying non-stationary  ... 
arXiv:2105.03584v4 fatcat:7trf3viumzhbxegglgv6mzz6de

First plant cell atlas workshop report

Selena Rice, Emily Fryer, Suryatapa Ghosh Jha, Andrey Malkovskiy, Heather Meyer, Jason Thomas, Renee Weizbauer, Kangmei Zhao, Kenneth Birnbaum, David Ehrhardt, Zhiyong Wang, Seung Y. Rhee (+1 others)
2020 Plant Direct  
The workshop featured invited talks to share initial data, along with broader ideas for the PCA.  ...  dynamics.  ...  K E Y W O R D S data science, live imaging, nanotechnology, plant cell atlas, proteomics, single-cell sequencing F I G U R E 1 First PCA Workshop participant demographics.  ... 
doi:10.1002/pld3.271 pmid:33083684 pmcid:PMC7557347 fatcat:sgq6mcyfnnabrh6m233ujj2mtu

Learning the Macroscopic Flow Model of Short Fiber Suspensions from Fine-Scale Simulated Data

Minyoung Yun, Clara Argerich Martin, Pierre Giormini, Francisco Chinesta, Suresh Advani
2019 Entropy  
Fiber–fiber interaction plays an important role in the evolution of fiber orientation in semi-concentrated suspensions.  ...  In this paper we do not address a new proposal of a fiber interaction model, but a data-driven methodology able to enrich existing models from data, that in our case comes from a direct numerical simulation  ...  Figure 3 . 3 Numerical results Figure 4 . 4 Time evolution of the data-based enriched Jeffery solution (dotted line) with respect to the molecular dynamics (MD) solution (continuous) (a,b) andȧ dev 11  ... 
doi:10.3390/e22010030 pmid:33285805 fatcat:hkckqxsy5zdh5ncekrj7i2bs4q

A narrative review of MRI acquisition for MR-guided-radiotherapy in prostate cancer

Jing Yuan, Darren M. C. Poon, Gladys Lo, Oi Lei Wong, Kin Yin Cheung, Siu Ki Yu
2021 Quantitative Imaging in Medicine and Surgery  
Diagnostic prostate MRI has been relatively familiar in the community, particularly with the development of Prostate Imaging - Reporting and Data System (PI-RADS).  ...  Magnetic resonance guided radiotherapy (MRgRT), enabled by the clinical introduction of the integrated MRI and linear accelerator (MR-LINAC), is a novel technique for prostate cancer (PCa) treatment, promising  ...  Regarding MRI acquisition in PCa MRgRT, deep learning can greatly further accelerate acquisition by learning spatial and/or temporal dependencies of image appearance from the highly under sampled MRI k-space  ... 
doi:10.21037/qims-21-697 pmid:35111651 pmcid:PMC8739116 fatcat:3jrf7nrlvbeljllf5ynayetsii

Vision, challenges and opportunities for a Plant Cell Atlas

Plant Cell Atlas Consortium, Jahed Ahmed, Oluwafemi Alaba, Gazala Ameen, Vaishali Arora, Mario A Arteaga-Vazquez, Alok Arun, Julia Bailey-Serres, Laura E Bartley, George W Bassel, Dominique C Bergmann, Edoardo Bertolini (+137 others)
2021 eLife  
This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses.  ...  A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA  ...  Acknowledgements The PCA community-building activities are funded in part by the National Science Foundation grant numbers MCB-1916797 and MCB-2052590  ... 
doi:10.7554/elife.66877 pmid:34491200 pmcid:PMC8423441 fatcat:xq6vypwp4rbbjfkhtl5l2ty35q

Sniffing fungi – phenotyping of volatile chemical diversity in Trichoderma species

Yuan Guo, Werner Jud, Andrea Ghirardo, Felix Antritter, J. Philipp Benz, Jörg‐Peter Schnitzler, Maaria Rosenkranz
2020 New Phytologist  
The data mining strategy based on multivariate data analysis and machine learning allows uncovering the volatile metabolic fingerprints.  ...  Our data revealed dynamic, development-dependent and extremely species-specific VOC profiles from the biocontrol genus Trichoderma.  ...  YG performed the experiments and analyzed the data with help from WJ. YG and WJ contributed equally. YG prepared the figures and, together with MR, wrote the manuscript.  ... 
doi:10.1111/nph.16530 pmid:32155672 fatcat:7xbblv7pgjatpkyfc63f77ks2y

Scalable Bayesian reduced-order models for high-dimensional multiscale dynamical systems [article]

P.S. Koutsourelakis, Elias Bilionis
2010 arXiv   pre-print
We discuss parallelizable, online inference and learning algorithms that employ Sequential Monte Carlo samplers and scale linearly with the dimensionality of the observed dynamics.  ...  While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and their applicability over  ...  When dealing with highdimensional molecular ensembles for example, each of these building blocks might be an (overdamped) Langevin equation with a harmonic potential [17, 80, 83] .  ... 
arXiv:1001.2753v2 fatcat:nckumc7n7nakbdpdo5bujepwva

On-Line Thermally Induced Evolved Gas Analysis: An Update—Part 1: EGA-MS

Roberta Risoluti, Giuseppina Gullifa, Laura Barone, Elena Papa, Stefano Materazzi
2022 Molecules  
The importance of an accurate interpretation of the thermally-induced reaction mechanism which involves the formation of gaseous species is necessary to obtain the characterization of the evolved products  ...  SAM evolved H 2 O data suggested the presence of an Fe-rich dioctahedral smectite, such as nontronite, in the sample from beneath the ridge.  ...  The data sets are subjected to mathematical decomposition based on PCA to selectively derive essential information. The EGA-MS-PCA process reveals the formation of a π-π stacking interaction [168] .  ... 
doi:10.3390/molecules27113518 pmid:35684458 fatcat:fsy7ny4p3zcardhc3xdyxvtroq
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