The deployment of deep convolutional neural networks (CNNs) in many real world applications
is largely hindered by their high computational cost. In this paper, we propose a novel …

Inspired by the gradient structures of biological materials, researchers have explored
compositional and structural gradients for about 40 years as an approach to enhance the …

Nowadays, energy-saving building materials are important for reducing indoor energy
consumption by enabling better thermal insulation, promoting effective sunlight harvesting and …

Deep neural networks are vulnerable to adversarial examples, which poses security concerns
on these algorithms due to the potentially severe consequences. Adversarial attacks …

Renewable and biodegradable materials derived from biomass are attractive candidates to
replace non-biodegradable petrochemical plastics. However, the mechanical performance …

Accurate prediction of the future given the past based on time series data is of paramount
importance, since it opens the door for decision making and risk management ahead of time. In …

We present Deeply Supervised Object Detector (DSOD), a framework that can learn object
detectors from scratch. State-of-the-art object objectors rely heavily on the off-the-shelf …

Background In late December 2019, an outbreak of acute respiratory illness, coronavirus
disease 2019 (COVID-19), emerged in Wuhan, China. We aimed to study the epidemiology, …

Antimicrobial peptides (AMPs) are promising next generation antibiotics that hold great
potential for combating bacterial resistance. AMPs can be both bacteriostatic and bactericidal, …

Rationale: Obstructive sleep apnea, a condition leading to chronic intermittent hypoxia (CIH),
is associated with hyperlipidemia, atherosclerosis, and a high cardiovascular risk. A causal …