On the Capacity of the Peak-Limited and Band-Limited Channel.

We investigate the peak-power limited (PPL) Additive White Gaussian Noise (AWGN) channels in which the signal is band-limited, and its instantaneous power cannot exceed the power . This model is relevant to many communication systems; however, its capacity is still unknown. We use a new geometry-based approach which evaluates the maximal entropy of the transmitted signal by assessing the volume of the body, in the space of Nyquist-rate samples, comprising all the points the transmitted signal can reach.

Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression.

Alzheimer's disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only in disease manifestations but also in different progression patterns, is critical for developing effective disease models that can be used in clinical and research settings. We introduce a machine learning model for identifying underlying patterns in Alzheimer's disease (AD) trajectory using longitudinal multi-modal data from the ADNI cohort and the AIBL cohort.

How can we reward you? A compliance and reward ontology (CaRO) for eliciting quantitative reward rules for engagement in mHealth app and healthy behaviors.

Objective: When developing mHealth apps with point reward systems, knowledge engineers and domain experts should define app requirements capturing quantitative reward patterns that reflect patient compliance with health behaviors. This is a difficult task, and they could be aided by an ontology that defines systematically quantitative behavior goals that address more than merely the recommended behavior but also rewards for partial compliance or practicing the behavior more than recommended. No ontology and algorithm exist for defining point rewards systematically.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task.