Open Heart

Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
Tech Xplore

Exposing biases, moods, personalities and abstract concepts hidden in large language models

By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they're far from simple answer-generators; they can also express abstract concepts, such as ...
UC San Diego Today

A New Method to Steer AI Output Uncovers Vulnerabilities and Potential Improvements

A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside these models. The new method could lead to more reliable, more efficient, ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
IEEE

Modeling and predictive control of networked systems via physics-informed neural networks

Abstract: This article addresses the data-driven modeling and predictive control of networked systems. The main contribution is a novel methodology to develop a physics-informed recurrent neural ...
AHA/ASA Journals

Multistage Electrocardiographic Profiling for Mental Stress-Induced Myocardial Ischemia in Women: A Prospective, Age-Matched Cohort Study in China

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...
IEEE

Practical Nonlinear Model Predictive Control With Kolmogorov–Arnold Network Models

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...