Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Prognostics and health management (PHM) is developed to guarantee the safety and
reliability of Lithium-ion (Li-ion) battery during operations. Due to the advantages of deep …

Spike sorting is the computational process of extracting the firing times of single neurons
from recordings of local electrical fields. This is an important but hard problem in …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Spike sorting is the computational process of extracting the firing times of single neurons
from recordings of local electrical fields. This is an important but hard problem in …

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

Significance: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to
distinguish the unique molecular environment of fluorophores. FLIM measures the time a …

The k-means algorithm is generally the most known and used clustering method. There are
various extensions of k-means to be proposed in the literature. Although it is an …

Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

Neural recordings using invasive devices in humans can elucidate the circuits underlying
brain disorders, but have so far been limited to short recordings from externalized brain …