Data provenance, or data lineage, describes the life cycle of data. In
s...
We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs...
Machine learning (ML) in healthcare presents numerous opportunities for
...
Personalized news recommender systems help users quickly find content of...
A critical challenge to image-text retrieval is how to learn accurate
co...
We propose FedScore, a privacy-preserving federated learning framework f...
Knowing HPC applications of jobs and analyzing their performance behavio...
The resource demands of HPC applications vary significantly. However, it...
Graph convolutional networks (GCNs) have been attracting widespread
atte...
Creating an essay based on a few given topics is a challenging NLP task....
Natural Language Processing (NLP) has been revolutionized by the use of
...
In many scenarios such as genome-wide association studies where dependen...
The Berry-Esséen upper bounds of moment estimators and least squares
est...
We investigate data-driven forward-inverse problems for Yajima-Oikawa (Y...
In Chen and Zhou 2021, they consider an inference problem for an
Ornstei...
Multivariate meta-analysis (MMA) is a powerful tool for jointly estimati...
The civil aviation community is actively exploring and developing the
so...
Objectives: This paper develops two algorithms to achieve federated
gene...
In this paper, we propose MGNet, a simple and effective multiplex graph
...
Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parame...
Given a directed graph G = (V, E), the k-path partition problem is to
fi...
Objective: Recently Doi et al. argued that risk ratios should be replace...
This manuscript describes the first challenge on Federated Learning, nam...
Studying the determinants of adverse pregnancy outcomes like stillbirth ...
Along with the rapid expansion of information technology and digitalizat...
In the last decade, the secondary use of large data from health systems,...
Studies of the effects of medical interventions increasingly take place ...
Quaternion singular value decomposition (QSVD) is a robust technique of
...
Multivariate regression techniques are commonly applied to explore the
a...
Accelerating deep model training and inference is crucial in practice.
E...
In meta-analyses, publication bias is a well-known, important and challe...
The validity of conclusions from meta-analysis is potentially threatened...
Practical problems with missing data are common, and statistical methods...
Publication bias occurs when the publication of research results depends...
According to Davey et al. (2011) with a total of 22,453 meta-analyses fr...
Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectra...
In multicenter research, individual-level data are often protected again...
The identification of spatial and temporal three-dimensional (3D) genome...
Spectral Clustering is a popular technique to split data points into gro...
Given a simple connected graph G = (V, E), we seek to partition the vert...
Efficient job scheduling on data centers under heterogeneous complexity ...
Efficient job scheduling on data centers under heterogeneous complexity ...
Nonparametric varying coefficient (NVC) models are widely used for model...
In this study, we proposed a convolutional neural network model for gend...
We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimati...
Currently, Burst buffer has been proposed to manage the SSD buffering of...
Given a graph G = (V, E), the 3-path partition problem is to find a
mini...
Given a simple graph G = (V, E) and a constant integer k > 2, the
k-path...
In many real-world applications, data are often unlabeled and comprised ...
A mixed shop is a manufacturing infrastructure designed to process a mix...