Toon Albers,Elena Lazovik,Mostafa Hadadian Nejad Yousefi et al.
Toon Albers et al.
Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations perf...
Dragi Kimovski,Roland Mathá,Gabriel Iuhasz et al.
Dragi Kimovski et al.
The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitig...
Corrigendum: Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples [0.03%]
勘误：样本的统计富集分析：一种注释生物样本元数据邻近区域的通用工具
Thanh M Nguyen,Samuel Bharti,Zongliang Yue et al.
Thanh M Nguyen et al.
[This corrects the article DOI: 10.3389/fdata.2021.725276.]. Keywords: SEAS; clinotype; glioblastoma multifo...
Published Erratum
Frontiers in big data. 2021 Nov 16:4:804141. DOI:10.3389/fdata.2021.804141 2021
Teeport: Break the Wall Between the Optimization Algorithms and Problems [0.03%]
Teeport：打破优化算法与问题之间的障碍
Zhe Zhang,Xiaobiao Huang,Minghao Song
Zhe Zhang
Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the al...
Anomaly Detection for the Centralised Elasticsearch Service at CERN [0.03%]
欧洲核子研究中心集中Elasticsearch服务的异常检测
Jennifer R Andersson,Jose Alonso Moya,Ulrich Schwickerath
Jennifer R Andersson
For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, deli...
The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers? [0.03%]
新老之争：物理信息深度学习能否取代传统线性方程求解器？
Stefano Markidis
Stefano Markidis
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged as a new essential tool to solve various...
Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog [0.03%]
利用综合eQTL目录系统探索复杂性状的组织-通路关联
Boqi Wang,James Yang,Steven Qiu et al.
Boqi Wang et al.
The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of ...
Fitting and Cross-Validating Cox Models to Censored Big Data With Missing Values Using Extensions of Partial Least Squares Regression Models [0.03%]
使用偏最小二乘回归模型的扩展拟合和交叉验证具有缺失值的大规模生存分析数据的Cox模型
Frédéric Bertrand,Myriam Maumy-Bertrand
Frédéric Bertrand
Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult. We proposed...
Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators [0.03%]
基于机器学习和多种干旱指标的美国干旱监测地图自动化分析
Pouyan Hatami Bahman Beiglou,Lifeng Luo,Pang-Ning Tan et al.
Pouyan Hatami Bahman Beiglou et al.
The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is b...
Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing [0.03%]
通过自动特征主题配对实现语义丰富的空间网络表示学习
Dongjie Wang,Kunpeng Liu,David Mohaisen et al.
Dongjie Wang et al.
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded f...