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OCEANS Hampton Roads Conference

作者： Scott, Nicholas Kukulka, Tobias Riverside Res Open Innovat Ctr Beavercreek OH 45431 USA Univ Delaware Ctr Appl Coastal Res Newark DE USA

ISBN: (数字)9781665468091

ISBN: (纸本)9781665468091

A latent-observational space analytical formulism is applied to a sub-grid modeled turbulent kinetic energy (tke) field emanating from ocean turbulence large eddy simulation (LES) data containing Langmuir cells but no breaking waves. The purpose of the analysis is to illustrate how machine learning modeling can be used to understand the probabilistic structure of observational space and how the allied latent space can be related statistically to it for the purpose of data generation. The Peter and Clark (pc)-algorithm-based Bayesian belief network (BBN) edge-nodal structure for the observational-space tke subdomains demonstrates a distinctive nonlocal connectivity pattern when the multidimensional scaling graph layout is invoked. When the Chow-Liu algorithm is used, tree-based connectivity in the network is revealed. In particular, a dominant parental root node occupies the far upper left region in the observational-space domain with many edge connections flowing toward the right. Hidden Markov model (HMM) parameter estimation, applied to the maximum and minimum values taken from the mean tke feature matrix and to generative topographic mapping (GTM)-based latent space for the same tke feature nodes, enables estimation of the latent-space state transition matrix and tke latent-observational space emission matrices. The latent-space state transition matrix demonstrates how many columns of latent space, associated with different root-mean-square tke values, possess a high probability of transitioning to other distinct latent-space areas. The tke latent-observational space emission matrix provides spatial subdomain locations most strongly tied to specific vertical columns of GTM-based latent space. The maximum value-based emission matrix shows very high probabilities for single columns on the latent-space perimeter being associated with maximum values occurring at specific observational-space subdomain locations. These observational-space nodal locations have strong st

关键词： ocean turbulence large eddy simulation turbulent kinetic energy generative topographic mapping Bayesian belief network pc algorithm Chow-Liu algorithm Hidden Markov models emission matrix

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GPUCSL: GPU-Based Library for Causal Structure Learning 22

GPUCSL: GPU-Based Library for Causal Structure Learning

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22nd IEEE International Conference on Data Mining (ICDM)

作者： Braun, Tom Hurdelhey, Ben Meier, Dominik Tsayun, Petr Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9798350346091

GPUCSL is a maintainable and extensible Python library for GPU-accelerated causal structure learning (CSL) based on the pc algorithm. The library supports multivariate normal and discrete distributed data, and implements multi-GPU support for multivariate normal distributed data. GPUCSL combines several stand-alone independent research implementations to allow a unified entry point into GPU-accelerated CSL. The library outperforms CPU-based implementations with an average speedup factor of 9.5 against pcalg and 19.8 against bnlearn and remains within the order of magnitude of existing GPU-accelerated CSL research. The source code is available at https://***/hpi- epic/gpucsl.

关键词： Causal Structure Learning pc algorithm GPU Acceleration CUDA Python

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GPU-accelerated constraint-based causal structure learning for discrete data

GPU-accelerated constraint-based causal structure learning f...

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2021 SIAM International Conference on Data Mining, SDM 2021

作者： Hagedorn, Christopher Huegle, Johannes Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (纸本)9781611976700

Learning the causal structures from high-dimensional observational data is an omnipresent challenge in data science. State-of-the-art methods for constraint-based Causal Structure Learning (CSL) apply conditional independence (CI) tests to determine the underlying causal structures. In the context of discrete data, each CI test requires calculating the marginals over contingency tables based on the respective observations. This calculation leads to long overall execution times. In our work, we propose a parallel execution strategy tailored for constraint-based CSL on a Graphics Processing Unit (GPU) to accelerate the execution for discrete data. Hence, we introduce the gpupc algorithm that performs all CI tests on a GPU and extends the existing parallel execution strategy for constraint-based CSL by calculating the marginals over contingency tables within units of threads. Further, gpupc implements explicit memory management to handle the corresponding auxiliary data structures in GPU memory. An experimental evaluation shows that gpupc scales well even for higher-dimensional settings, with auxiliary data structures exceeding on-chip memory. In particular, running on NVIDIA Tesla V100 hardware gpupc outperforms a GPU baseline by factors of up to 45.6 and further outperforms existing parallel CPU-based implementations running on 40 cores by a factor of 62.1. © 2021 by SIAM.

关键词： Bayesian networks Causal structure learning Discrete data GPU pc algorithm

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Causal discovery of gene regulation with incomplete data

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2020年第4期183卷 1747-1775页

作者： Foraita, Ronja Friemel, Juliane Guenther, Kathrin Behrens, Thomas Bullerdiek, Joern Nimzyk, Rolf Ahrens, Wolfgang Didelez, Vanessa Leibniz Inst Prevent Res & Epidemiol BIPS Achterstr 30 D-28359 Bremen Germany Univ & Univ Hosp Zurich Zurich Switzerland Ruhr Univ Bochum Bochum Germany Ctr Human Genet Bremen Germany Univ Bremen Bremen Germany

Causal discovery algorithms aim to identify causal relations from observational data and have become a popular tool for analysing genetic regulatory systems. In this work, we applied causal discovery to obtain novel insights into the genetic regulation underlying head-and-neck squamous cell carcinoma. Some methodological challenges needed to be resolved first. The available data contained missing values, but most approaches to causal discovery require complete data. Hence, we propose a new procedure combining constraint-based causal discovery with multiple imputation. This is based on using Rubin's rules for pooling tests of conditional independence. A second challenge was that causal discovery relies on strong assumptions and can be rather unstable. To assess the robustness of our results, we supplemented our investigation with sensitivity analyses, including a non-parametric bootstrap to quantify the variability of the estimated causal structures. We applied these methods to investigate how the high mobility group AT-Hook 2 (HMGA2) gene is incorporated in the protein 53 signalling pathway playing an important role in head-and-neck squamous cell carcinoma. Our results were quite stable and found direct associations between HMGA2 and other relevant proteins, but they did not provide clear support for the claim that HMGA2 itself is a key regulator gene.

关键词： Gene expression Graphical models Head-and-neck squamous cell carcinoma HMGA2 gene Human papilloma-virus pc algorithm Protein 53 signalling pathway

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Dynamics analysis of factors affecting electricity consumption fluctuations based on economic conditions: Application of SVAR and TVP-VAR models

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ENERGY 2021年 226卷 120340-120340页

作者： Esmaeili, Parisa Rafei, Meysam Kharazmi Univ Fac Econ Tehran Iran

Iran is known as a high electricity-consuming country that has an undeniable share in total energy consumption. Therefore, we intend to scrutinize the impact of structural shocks of GDP, oil revenues, and inflation on Iran's electricity consumption. Thereby, we combined SVAR with DAG technique to describe economic structures and Markov-switching in line with the TVP-VAR model to examine Iran style facts. The SVAR model results represent that inflation shocks have a significant effect on electricity consumption, while the effects of GDP and oil revenues are insignificant. Meanwhile, the most effective variable on electricity consumption fluctuations is inflation, which affects electricity consumption negatively and strongly. TVP-VAR model results show that electricity consumption is affected by structural shocks of variables differently depending on Iran's economic conditions. Thereby, electricity consumption responds positively to the shocks of variables in most periods and years. Since 2011, when sanctions were imposed on Iran, the effect of structural shocks of variables on electricity consumption has become weaker. Finally, we proposed to policymakers to adopt energy conservation policies based on Iran's economic and political realities. Iran must also reduce its dependence on oil revenues and imported energy equipment so that sanctions do not significantly affect the country's economy. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Electricity consumption Shock SVAR TVP-VAR Markov-switching pc algorithm

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Out-of-Core GPU-Accelerated Causal Structure Learning 19th

Out-of-Core GPU-Accelerated Causal Structure Learning

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19th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Schmidt, Christopher Huegle, Johannes Horschig, Siegfried Uflacker, Matthias Univ Potsdam Enterprise Platform & Integrat Concepts Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783030389918;9783030389901

Learning the causal structures in high-dimensional datasets enables deriving advanced insights from observational data. For example, the construction of gene regulatory networks inferred from gene expression data supports solving biological and biomedical problems, such as, in drug design or diagnostics. With the adoption of Graphics Processing Units (GPUs) the runtime of constraint-based causal structure learning algorithms on multivariate normal distributed data is significantly reduced. For extremely high-dimensional datasets, e.g., provided by The Cancer Genome Atlas (TCGA), state-of-the-art GPU-accelerated algorithms hit the device memory limit of single GPUs and consequently, execution fails. In order to overcome this limitation, we propose an out-of-core algorithm for GPU-accelerated constraint-based causal structure learning on multivariate normal distributed data. We experimentally validate the scalability of our algorithm, beyond GPU device memory capacities and compare our implementation to a baseline using Unified Memory (UM). In recent GPU generations, UM overcomes the device memory limit, by utilizing the GPU page migration engine. On a real-world gene expression dataset from the TCGA, our approach outperforms the baseline by a factor of 95 and is faster than a parallel Central Processing Unit (CPU)-based version by a factor of 236.

关键词： GPU-acceleration Out-of-core Causal structure learning pc algorithm

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Inferring functional connectivity in MRI using Bayesian network structure learning with a modified pc algorithm

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NEUROIMAGE 2013年 75卷 165-175页

作者： Iyer, Swathi P. Shafran, Izhak Grayson, David Gates, Kathleen Nigg, Joel T. Fair, Damien A. Oregon Hlth & Sci Univ Dept Behav Neurosci Portland OR 97239 USA Oregon Hlth & Sci Univ Dept Psychiat Portland OR 97239 USA Oregon Hlth & Sci Univ Adv Imaging Res Ctr Portland OR 97239 USA Oregon Hlth & Sci Univ Dept Biomed Engn Portland OR 97239 USA Univ Calif Davis Ctr Neurosci Davis CA 95616 USA Virginia Polytech Inst & State Univ Dept Psychol Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Arlington Innovat Ctr Blacksburg VA 24061 USA

Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al. (2011), and apply pc algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, pc algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the pc algorithm outperformed the direct correlations. We conclude that, under certain conditions, the pc algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations. (C) 2013 Elsevier Inc. All rights reserved.

关键词： fMRI Bayesian network pc algorithm Directed functional connectivity Effective connectivity

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Joint skeleton estimation of multiple directed acyclic graphs for heterogeneous population

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BIOMETRICS 2019年第1期75卷 36-47页

作者： Liu, Jianyu Sun, Wei Liu, Yufeng Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27515 USA Fred Hutchinson Canc Res Ctr Div Publ Hlth Sci Biostat Program 1124 Columbia St Seattle WA 98104 USA Univ N Carolina Lineberger Comprehens Canc Ctr Dept Genet Dept BiostatCarolina Ctr Genome Sci Chapel Hill NC 27515 USA

The directed acyclic graph (DAG) is a powerful tool to model the interactions of high-dimensional variables. While estimating edge directions in a DAG often requires interventional data, one can estimate the skeleton of a DAG (i.e., an undirected graph formed by removing the direction of each edge in a DAG) using observational data. In real data analyses, the samples of the high-dimensional variables may be collected from a mixture of multiple populations. Each population has its own DAG while the DAGs across populations may have significant overlap. In this article, we propose a two-step approach to jointly estimate the DAG skeletons of multiple populations while the population origin of each sample may or may not be labeled. In particular, our method allows a probabilistic soft label for each sample, which can be easily computed and often leads to more accurate skeleton estimation than hard labels. Compared with separate estimation of skeletons for each population, our method is more accurate and robust to labeling errors. We study the estimation consistency for our method, and demonstrate its performance using simulation studies in different settings. Finally, we apply our method to analyze gene expression data from breast cancer patients of multiple cancer subtypes.

关键词： Bi-level selection DAG pc algorithm Probabilistic labels Soft classifier

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On Using the pc algorithm for Learning Continuous Bayesian Networks: An Experimental Analysis

On Using the PC Algorithm for Learning Continuous Bayesian N...

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15th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Fernandez, Antonio Perez-Bernabe, Inmaculada Salmeron, Antonio Univ Almeria Dept Math E-04120 Almeria Spain

ISBN: (纸本)9783642406423;9783642406430

Mixtures of truncated basis functions (MoTBFs) have been recently proposed as a generalisation of mixtures of truncated exponentials and mixtures of polynomials for modelling conditional distributions in hybrid Bayesian networks. However, no structural learning algorithm has been proposed so far for such models. In this paper we investigate the use of the pc algorithm as a means of obtaining the underlying network structure, that is finally completed by plugging in the conditional MoTBF densities. We show through a set of experiments that the approach is valid and competitive with current alternatives of discretizing the variables or adopting a Gaussian assumption. We restrict the scope of this work to continuous variables.

关键词： continuous Bayesian networks pc algorithm mixtures of truncated basis functions structural learning

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Causality Relationship Among Attributes Applied in an Educational Data Set 19

Causality Relationship Among Attributes Applied in an Educat...

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34th ACM/SIGAPP Annual International Symposium on Applied Computing (SAC)

作者： de Carvalho, Walisson Ferreira Zarate, Luis Enrique Ctr Univ Una Belo Horizonte MG Brazil Pontificia Univ Catolica Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9781450359337

This paper presents Causal Inference theory applied in education area. It were used data set extracted from the Learning Management System Moodle containing actions performed by the students during 20 weeks that lasted the online course of algorithm and Data Structure. After being pre processed, data were submitted to causal structure search using pc algorithm and to interventions using do-calculus method, resulting in computed causal effects of actions on the performance of the students.

关键词： Causal Inference Bayesian Network Causal Structure Search Causal Effect Intervention pc algorithm do-calculus

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