Abstract: The traditional extreme learning machine (ELM) approach is based on a random assignment of the hidden weight values, while the linear coefficients of the output layer are determined ...
2 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 3 Center for Clinical Trials Innovation, ...
Abstract: The performance of data-driven fault diagnosis methods is often impeded by irrelevant and redundant information in high-dimensional data. To address this issue, this article proposes a fault ...
In a standard paper assignment setting, a set $\mathcal{P}$ of $n^{(p)}$ papers needs to be assigned to a set $\mathcal{R}$ of $n^{(r)}$ reviewers. To ensure each ...
Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, ...
Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi’an 710127, China ...
PPA-GCN is a deep learning framework based on a graph convolutional model (Figure 1). Gene synteny information from the selected genome is used to construct edges in a network, while genes sharing ...
The problem of optimizing over random structures emerges in many areas of science and engineering, ranging from statistical physics to machine learning and artificial intelligence. For many such ...
Editor’s note: This blog post is the second in a series providing an overview and history of LinkedIn’s experimentation platform. The previous post on the history of LinkedIn’s experimentation ...
Metabolomics characterizes metabolites with high-throughput techniques. Recently, liquid chromatography mass spectrometry (LC-MS) has become widely adopted by the metabolomics community as a powerful ...
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