Neural activity patterns in randomly sampled neuron groups statistically match whole-brain dynamics, revealing a scale-invariant organizational principle that enables robust and efficient computation ...

This was achieved by deriving representational ECG features using a variational autoencoder (VAE) and applying dimensionality reduction via learning a tree (DDRTree) 12 to create a compact, ...

We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver that leverages a ...

Palmoplantar pustulosis (PPP) has a significant impact on patients’ overall quality of life (QOL), with younger age and nail involvement in these patients associated with increased disease area and ...

Dimensionality reduction excels at handling large volumes of data, a common scenario in most thermometric essays. In essence, DR transforms data from a high-dimensional space into a low-dimensional ...

We model noise reduction using a deep convolutional autoencoder. The input is a noisy density matrix split into real and imaginary parts with shape (32, 32, 2).

This new algorithm (IFK), when compared against well-known algorithms such as Reverse-Cuthill-McKee (RCM), Gibbs (GBS), Gibbs-Poole-Stockmeyer (GPS) and Sloan’s (SLN) over 101 test cases from the ...