Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of
genomics and environment on plants, limiting the progress of smart breeding and precise …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Attribution methods are a popular class of explainability methods that use heatmaps to
depict the most important areas of an image that drive a model decision. Nevertheless …

Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …

Hyperspectral super-resolution refers to the problem of fusing a hyperspectral image (HSI)
and a multispectral image (MSI) to produce a super-resolution image (SRI) that admits fine …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Self-supervised learning allows AI systems to learn effective representations from large
amounts of data using tasks that do not require costly labeling. Mode collapse, ie, the model …

The spectrum cartography (SC) technique constructs multi-domain (eg, frequency, space,
and time) radio frequency (RF) maps from limited measurements, which can be viewed as …

Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

The rapid growth of large-scale spatial gene expression data demands efficient and reliable
computational tools to extract major trends of gene expression in their native spatial context …