In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially
outperforms training from scratch. When using fine-tuning, the underlying assumption is that …

This paper provides an initial investigation on the application of convolutional neural networks
(CNNs) for fingerprint-based positioning using measured massive MIMO channels. When …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Fixed dose combination (FDC) products are common in the treatment of hypertension, diabetes,
human immunodeficiency virus, and tuberculosis. They make it possible to combine two …

Various drug delivery approaches can be used to maximize therapeutic efficacy and minimize
side effects, by impacting absorption, distribution, metabolism, and elimination (ADME) of …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Miniaturized spectrometers are of considerable interest for their portability. Most designs to
date employ a photodetector array with distinct spectral responses or require elaborated …

In order to improve both performance and stability of perovskite solar cells, a design is
provided by combining the advantages of high‐efficiency 3D perovskite solar cells (PSCs) and …

Several studies have demonstrated that estrogen receptor α (ESR1) participates in the
pathogenesis of persistent hepatitis B virus (HBV) infection. To examine whether polymorphisms …

Fine-tuning pre-trained deep networks is a practical way of benefiting from the representation
learned on a large database while having relatively few examples to train a model. This …