In this work, we investigate the positional encoding methods used in language pre-training
(eg, BERT) and identify several problems in the existing formulations. First, we show that in …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

In this paper we introduce a simple heuristic adaptive restart technique that can dramatically
improve the convergence rate of accelerated gradient schemes. The analysis of the …

A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional
discrete signals, by solving the total variation regularized least-squares problem or the …

Multi-task learning (MTL) aims to improve the performance of multiple related tasks by
exploiting the intrinsic relationships among them. Recently, multi-task feature learning …

Fine-scale rates of meiotic recombination vary by orders of magnitude across the genome
and differ between species and even populations. Studying cross-population differences …

Alzheimer's disease (AD), the most common type of dementia, is a severe
neurodegenerative disorder. Identifying biomarkers that can track the progress of the …

We study the problem of estimating high-dimensional regression models regularized by a
structured sparsity-inducing penalty that encodes prior structural information on either the …

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging
datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging …

Multi-task learning models based on temporal smoothness assumption, in which each time
point of a sequence of time points concerns a task of prediction, assume the adjacent tasks …