NPARS-A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science [0.03%]
NPARS-解决基因组数据科学中准确性和可重复性问题的新方法
Li Ma,Erich A Peterson,Ik Jae Shin et al.
Li Ma et al.
Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating mat...
Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples [0.03%]
样品的统计富集分析：一个给生物样品元数据邻域注释的通用工具
Thanh M Nguyen,Samuel Bharti,Zongliang Yue et al.
Thanh M Nguyen et al.
Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biom...
TSI-GNN: Extending Graph Neural Networks to Handle Missing Data in Temporal Settings [0.03%]
TSI-GNN：扩展图神经网络以处理时间设置中的缺失数据
David Gordon,Panayiotis Petousis,Henry Zheng et al.
David Gordon et al.
We present a novel approach for imputing missing data that incorporates temporal information into bipartite graphs through an extension of graph representation learning. Missing data is abundant in several domains, particularly when observa...
Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering [0.03%]
科学实验的数据生态系统：在化工领域管理燃烧实验和模拟分析
Edoardo Ramalli,Gabriele Scalia,Barbara Pernici et al.
Edoardo Ramalli et al.
The development of scientific predictive models has been of great interest over the decades. A scientific model is capable of forecasting domain outcomes without the necessity of performing expensive experiments. In particular, in combustio...
Digital Support for Renal Patients Before and During the COVID-19 Pandemic: Examining the Efforts of Singapore Social Service Agencies in Facebook [0.03%]
数字支持对肾病患者在COVID-19大流行前后的帮助：考察新加坡社交服务机构在Facebook上的努力
Junjie Tan,Aravind Sesagiri Raamkumar,Hwee Lin Wee
Junjie Tan
During the coronavirus disease 2019 (COVID-19) pandemic, social service agencies (SSAs) play a crucial role in supporting renal patients, who are particularly vulnerable to infections. Social media platforms such as Facebook, serves as an e...
Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series [0.03%]
基于因子的大规模时间序列多变量多步预测框架
Jacopo De Stefani,Gianluca Bontempi
Jacopo De Stefani
State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) are instead shifting the focus to problems chara...
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation, and Optimal Measurement Placement in Power Systems? [0.03%]
用于故障后定位，动态状态估计算及最优测量配置的神经网络选择研究
Andrei Afonin,Michael Chertkov
Andrei Afonin
We consider a power transmission system monitored using phasor measurement units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically the pre-fault st...
Towards Machine-Readable (Meta) Data and the FAIR Value for Artificial Intelligence Exploration of COVID-19 and Cancer Research Data [0.03%]
迈向机器可读的（元）数据以及FAIR价值在探索COVID-19和癌症研究数据的人工智能中的应用
Maria Luiza M Campos,Eugênio Silva,Renato Cerceau et al.
Maria Luiza M Campos et al.
Structural Compression of Convolutional Neural Networks with Applications in Interpretability [0.03%]
卷积神经网络的结构压缩及在可解释性中的应用
Reza Abbasi-Asl,Bin Yu
Reza Abbasi-Asl
Deep convolutional neural networks (CNNs) have been successful in many tasks in machine vision, however, millions of weights in the form of thousands of convolutional filters in CNNs make them difficult for human interpretation or understan...