In-Hospital Mortality Risk Factor Analysis in Multivessel Percutaneous Coronary Intervention Inpatient Recipients in the United States.

Objectives The primary goal of our study is to evaluate the mortality rate in inpatient recipients of multivessel percutaneous coronary intervention (MVPCI) and to evaluate the demographic risk factors and medical complications that increase the risk of in-hospital mortality. Methods We conducted a cross-sectional study using the Nationwide Inpatient Sample (NIS, 2016) and included 127,145 inpatients who received MVPCI as a primary procedure in United States' hospitals. We used a multivariable logistic regression model adjusted for demographic confounders to measure the odds ratio (OR) of association of medical complications and in-hospital mortality risk in MVPCI recipients.

Low-shot transfer with attention for highly imbalanced cursive character recognition.

Recognition of ancient Korean-Chinese cursive character (Hanja) is a challenging problem mainly because of large number of classes, damaged cursive characters, various hand-writing styles, and similar confusable characters. They also suffer from lack of training data and class imbalance issues. To address these problems, we propose a unified Regularized Low-shot Attention Transfer with Imbalance τ-Normalizing (RELATIN) framework.

A multimodal convolutional neuro-fuzzy network for emotion understanding of movie clips.

Multimodal emotion understanding enables AI systems to interpret human emotions. With accelerated video surge, emotion understanding remains challenging due to inherent data ambiguity and diversity of video content. Although deep learning has made a considerable progress in big data feature learning, they are viewed as deterministic models used in a "black-box" manner which does not have capabilities to represent inherent ambiguities with data.

Adaptive estimation of EEG-rhythms for optimal band identification in BCI.

The amplitude of EEG μ-rhythm is large when the subject does not perform or imagine movement and attenuates when the subject either performs or imagines movement. The knowledge of EEG individual frequency components in the time-domain provides useful insight into the classification process. Identification of subject-specific reactive band is crucial for accurate event classification in brain-computer interfaces (BCI).