State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation.

Background: Under the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace.

Cross-Modal Knowledge Adaptation for Language-Based Person Search.

In this paper, we present a method named Cross-Modal Knowledge Adaptation (CMKA) for language-based person search. We argue that the image and text information are not equally important in determining a person's identity. In other words, image carries image-specific information such as lighting condition and background, while text contains more modal agnostic information that is more beneficial to cross-modal matching.

Electroencephalography Classification in Brain-Computer Interface with Manifold Constraints Transfer.

Insufficient training data is a serious problem in all domains related to bioinformatics. Transfer learning is a promising tool to solve this problem, which relaxes the hypothesis that training data must be independent and identically distributed with the test data. We construct a sophisticated electroencephalography (EEG) signal representation and obtain an efficient EEG feature extractor through manifold constraints-based joint adversarial training with training data from other domains.

Low-Rank Linear Dynamical Systems for Motor Imagery EEG.

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification.