Adversarial training and attribution methods enable evaluation of robustness and interpretability of deep learning models for image classification.

Deep learning models have achieved high performance in a wide range of applications. Recently, however, there have been increasing concerns about the fragility of many of those models to adversarial approaches and out-of-distribution inputs. A way to investigate and potentially address model fragility is to develop the ability to provide interpretability to model predictions.

Forecasting the evolution of fast-changing transportation networks using machine learning.

Transportation networks play a critical role in human mobility and the exchange of goods, but they are also the primary vehicles for the worldwide spread of infections, and account for a significant fraction of CO emissions. We investigate the edge removal dynamics of two mature but fast-changing transportation networks: the Brazilian domestic bus transportation network and the U.S.

Role of Toll-like receptor 4 in inflammation-induced preterm delivery.

The aim of the present study was to investigate the potential role of Toll-like receptor 4 (TLR4) in lipopolysaccharide (LPS)-induced preterm delivery. Intraperitoneal injection of LPS in the presence or absence of previous TLR4 blockade was performed to establish a murine model of preterm delivery. The incidences of preterm delivery and fetal death were calculated.