1. Software for the simulation study in "Data-driven formulation of the Kalman filter and its Application to Predictive Control" 

   Smith, Roy (2024)

   Data-driven methods for predictive control rely on input-output data to give a Hankel matrix representation of the set of system behaviours. They are poorly suited to situations where both process noise and measurement noise dominate the behaviour whereas Kalman filters optimally estimate system states in this scenario. We derive a data-driven Kalman filter formulation based on dynamic evolution of Hankel matrix output predictions. ...

   Software

2. The genomic potential of photosynthesis in piconanoplankton is functionally redundant but taxonomically structured at a global scale 

   Schickele, Alexandre; Debeljak, Pavla; Ayata, Sakina-Dorothée; et al. (2024)

   Science Advances

   Carbon fixation is a key metabolic function shaping marine life, but the underlying taxonomic and functional diversity involved is only partially understood. Using metagenomic resources targeted at marine piconanoplankton, we provide a reproducible machine learning framework to derive the potential biogeography of genomic functions through the multi-output regression of gene read counts on environmental climatologies. Leveraging the Marine ...

   Journal Article

3. Molecular Transducers of Physical Activity Consortium (MoTrPAC): human studies design and protocol 

   Jakicic J.M.; Kohrt W.M.; Houmard J.A.; et al. (2024)

   Journal of Applied Physiology

   Physical activity, including structured exercise, is associated with favorable health-related chronic disease outcomes. Although there is evidence of various molecular pathways that affect these responses, a comprehensive molecular map of these molecular responses to exercise has not been developed. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is a multicenter study designed to isolate the effects of structured ...

   Journal Article

4. Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning 

   De Ryck T.; Mishra S. (2024)

   Acta Numerica

   Physics-informed neural networks (PINNs) and their variants have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. This article aims to provide a comprehensive review of currently available results on the numerical analysis of PINNs and related models that constitute the backbone of physics-informed machine learning. We provide a unified ...

   Journal Article

5. MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images 

   Naumova K.; Devos A.; Karimireddy S.P.; et al. (2024)

   npj Digital Medicine

   Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness. This study presents MyThisYourThat (MyTH) approach, which adapts an interpretable prototypical part learning network to a distributed ...

   Journal Article

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