Adaptive Deep Fourier Residual method via overlapping domain decomposition
Authors: Jamie M. Taylor,Manuela Bastidas,Victor M. Calo,David Pardo
The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural networks (VPINNs). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on approximating the dual norm of the weak residual of a PDE.…
Elsevier BV
Steady solutions for the Schrödinger map equation
Authors: Claudia García,Luis Vega
In this paper we use bifurcation methods to construct a new family of solutions of the binormal flow, also known as the vortex filament equation, which do not change their form. Our examples are complementary to those obtained by S. Kida in 1981, and therefore they are also related, thanks to the…
Informa UK Limited
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
Authors: Yang Nan,Javier Del Ser,Zeyu Tang,Peng Tang,Xiaodan Xing,Yingying Fang,Francisco Herrera,Witold Pedrycz,Simon Walsh,Guang Yang
12 pages, 5 figures, Submitted to IEEE TNNLS Institute of Electrical and Electronics Engineers (IEEE)
This paper presents a novel Control Co-Design (CCD) methodology aimed at economically optimising the layout of wave energy converter (WEC) arrays. CCD ensures the synergy of optimised WEC and array parameters with the final control strategy, resulting in a comprehensive and efficient design of…
Elsevier BV
Imaginary past of a quantum particle moving on imaginary time
Authors: A. Uranga,E. Akhmatskaya,D. Sokolovski
The analytical continuation of classical equations of motion to complex times suggests that a tunneling particle spends in the barrier an imaginary duration i|T|. Does this…
American Physical Society (APS)
Robust Variational Physics-Informed Neural Networks
Authors: Sergio Rojas,Paweł Maczuga,Judit Muñoz-Matute,David Pardo,Maciej Paszyński
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the PDE problem: the trial space is a (Deep) Neural Network (DNN) manifold, while…
Elsevier BV
Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Authors: Xiaodan Xing,Huanjun Wu,Lichao Wang,Iain Stenson,May Yong,Javier Del Ser,Simon Walsh,Guang Yang
Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However, access to high-quality datasets is limited by the technical…
Association for Computing Machinery (ACM)
Fast $K$-Medoids With the $l_{1}$-Norm
Authors: Marco Capó,Aritz Pérez,Jose A. Lozano
K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the obtained solutions strongly depends upon their initialization phase. In…
Institute of Electrical and Electronics Engineers (IEEE)
Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest
Authors: Jon Urteaga,Andoni Elola,Anders Norvik,Eirik Unneland,Trygve C. Eftestøl,Abhishek Bhardwaj,David Buckler,Benjamin S. Abella,Eirik Skogvoll,Elisabete Aramendi
BACKGROUND: During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25-42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous…
Elsevier BV
Dynamical Phenomena in the Martian Atmosphere Through Mars Express Imaging
Authors: A. Sánchez-Lavega,T. del Río-Gaztelurrutia,A. Spiga,J. Hernández-Bernal,E. Larsen,D. Tirsch,A. Cardesin-Moinelo,P. Machado
AbstractThis review describes the dynamic phenomena in the atmosphere of Mars that are visible in images taken in the visual range through cloud formation and dust lifting. We describe the properties of atmospheric features traced by aerosols covering a large range of spatial and temporal scales,…
Springer Science and Business Media LLC
Grounding spatial relations in text-only language models
Authors: Gorka Azkune,Ander Salaberria,Eneko Agirre
This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of objects and they are properly trained to leverage those locations. We perform experiments on a verbalized version of the…
Elsevier BV
Cardiac output estimation using ballistocardiography: a feasibility study in healthy subjects
Authors: Svensøy, Johannes Nordsteien,Alonso, Erik,Elola Artano, Andoni,Bjørnerheim, Reidar,Ræder, Johan,Aramendi Ecenarro, Elisabete,Wik, Lars
AbstractThere is no reliable automated non-invasive solution for monitoring circulation and guiding treatment in prehospital emergency medicine. Cardiac output (CO) monitoring might provide a solution, but CO monitors are not feasible/practical in the prehospital setting. Non-invasive…
Springer Science and Business Media LLC
Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images
Authors: Xiujian Liu,Tianyuan Feng,Weipeng Liu,Liang Song,Yixuan Yuan,William Kongto Hau,Javier Del Ser,Zhifan Gao
Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the…
Institute of Electrical and Electronics Engineers (IEEE)
A probabilistic generative model to discover the treatments of coexisting diseases with missing data
Authors: Onintze Zaballa,Aritz Pérez,Elisa Gómez-Inhiesto,Teresa Acaiturri-Ayesta,Jose A. Lozano
Comorbidities, defined as the presence of co-existing diseases, progress through complex temporal patterns among patients. Learning such dynamics from electronic health records is crucial for understanding the coevolution of diseases. In general, medical records are represented through temporal…
Elsevier BV