Progress in dementia research has been limited, with substantial gaps in our knowledge of
targets for prevention, mechanisms for disease progression, and disease-modifying …

Recently, there has been an increased interest in the development of kernel methods for
learning with sequential data. The signature kernel is a learning tool with the potential to …

Background and objective Alzheimer's disease (AD) is the most common type of dementia
that can seriously affect a person's ability to perform daily activities. Estimates indicate that …

We study the complexity of isomorphism problems for tensors, groups, and polynomials.
These problems have been studied in multivariate cryptography, machine learning, quantum …

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with
or without neurological conditions. Applying machine learning (ML) models to gait analysis …

Distribution regression refers to the supervised learning problem where labels are only
available for groups of inputs instead of individual inputs. In this paper, we develop a …

Making predictions and quantifying their uncertainty when the input data is sequential is a
fundamental learning challenge, recently attracting increasing attention. We develop …

The development of machine learning algorithms in the cyber security domain has been
impeded by the complex, hierarchical, sequential and multimodal nature of the data …

The brain ventricles are structures that have been related to cognition since antiquity. They
are essential components in the development and maintenance of brain functions. The …

Simulation models often lack tractable likelihood functions, making likelihood-free inference
methods indispensable. Approximate Bayesian computation generates likelihood-free …