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Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFResponse Theory via Generative Score Modeling.
Phys Rev Lett
December 2024
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the generalized fluctuation-dissipation theorem. The methodology enables accurate estimation of system responses, including those with non-Gaussian statistics. We numerically validate our approach using time-series data from three different stochastic partial differential equations of increasing complexity: an Ornstein-Uhlenbeck process with spatially correlated noise, a modified stochastic Allen-Cahn equation, and the 2D Navier-Stokes equations.
View Article and Find Full Text PDFUsing tremendous photon statistics gained with the stray light aperture of the NuSTAR telescope over 11 years of operation, we set strong limits on the emission of close to monochromatic photons from the radiative decays of putative dark matter sterile neutrinos in the Milky Way. In the energy range of 3-20 keV covered by the NuSTAR, the obtained limits reach the bottom edge of theoretical predictions of realistic models where sterile neutrinos are produced in the early Universe. Only a small region is left to explore, if the sterile neutrinos form the entire dark matter component.
View Article and Find Full Text PDFExact Quantization of Topological Order Parameter in SU(N) Spin Models, N-ality Transformation and Ingappabilities.
Phys Rev Lett
December 2024
RIKEN, Condensed Matter Theory Laboratory, CPR, Wako, Saitama 351-0198, Japan.
We show that the ground-state expectation value of twisting operator is a topological order parameter for U(1)- and Z_{N}-symmetric symmetry-protected topological (SPT) phases in one-dimensional "spin" systems-it is quantized in the thermodynamic limit and can be used to identify different SPT phases and to diagnose phase transitions among them. We prove that this (nonlocal) order parameter must take values in Nth roots of unity, and its value can be changed by a generalized lattice translation acting as an N-ality transformation connecting distinct phases. This result also implies the Lieb-Schultz-Mattis (LSM) ingappability for SU(N) spins if we further impose a general translation symmetry.
View Article and Find Full Text PDFMachine-Learning Modeling of Elemental Ferroelectric Bismuth Monolayer.
Phys Rev Lett
December 2024
Xi'an Jiaotong University, School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials, Xi'an 710049, China.
The bismuth monolayer has recently been experimentally identified as a novel platform for the investigation of two-dimensional single-element ferroelectric system. Here, we model the potential energy surface of a bismuth monolayer by employing a message-passing neural network and achieve an error smaller than 1.2 meV per atom.
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