Separable Hamiltonian neural networks.

Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding a bias regarding the additive separability of the Hamiltonian reduces the regression complexity and improves regression performance. We propose separable HNNs that embed additive separability within HNNs using observational, learning, and inductive biases.

Influence of TikTok on Body Satisfaction Among Generation Z in Indonesia: Mixed Methods Approach.

Background: As social media platforms gain popularity, their usage is increasingly associated with cyberbullying and body shaming, causing devastating effects.

Objective: This study aims to investigate the impact of social media on Generation Z users' body image satisfaction. More specifically, it examines the impact of TikTok on body image satisfaction among TikTok users aged between 17 years and 26 years in Indonesia.

Impact of Social Media Usage on Users' COVID-19 Protective Behavior: Survey Study in Indonesia.

Background: Social media have become the source of choice for many users to search for health information on COVID-19 despite possible detrimental consequences. Several studies have analyzed the association between health information-searching behavior and mental health. Some of these studies examined users' intentions in searching health information on social media and the impact of social media use on mental health in Indonesia.

Transfer learning for scalability of neural-network quantum states.

Neural-network quantum states have shown great potential for the study of many-body quantum systems. In statistical machine learning, transfer learning designates protocols reusing features of a machine learning model trained for a problem to solve a possibly related but different problem. We propose to evaluate the potential of transfer learning to improve the scalability of neural-network quantum states.