Spin-induced scalarization and magnetic fields [PDF]
Physics Letters B, 2022 In the presence of certain non-minimal couplings between a scalar field and the Gauss-Bonnet curvature invariant, Kerr black holes can scalarize, as long as they are spinning fast enough.
Lorenzo Annulli +2 more
doaj +2 more sources
Higher dimensional black hole scalarization [PDF]
Journal of High Energy Physics, 2020 In the simplest scalar-tensor theories, wherein the scalar field is non-minimally coupled to the Ricci scalar, spontaneous scalarization of electrovacuum black holes (BHs) does not occur. This ceases to be true in higher dimensional spacetimes, d > 4. We
Dumitru Astefanesei +3 more
doaj +2 more sources
The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design [PDF]
Journal of CheminformaticsInverse molecular design imposes multiple property constraints, requiring them to be combined into a scalar reward for reinforcement-learning (RL) fine-tuning; a step that can be highly sensitive to the reward formulation. Here, we systematically analyze
Lucas Leuschner +1 more
doaj +2 more sources
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective [PDF]
Neural Information Processing Systems, 2023 Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recent years, there is a surge of interest in developing Specialized Multi-Task
Yuzheng Hu +5 more
semanticscholar +1 more source
In Defense of the Unitary Scalarization for Deep Multi-Task Learning [PDF]
Neural Information Processing Systems, 2022 Recent multi-task learning research argues against unitary scalarization, where training simply minimizes the sum of the task losses. Several ad-hoc multi-task optimization algorithms have instead been proposed, inspired by various hypotheses about what ...
Vitaly Kurin +4 more
semanticscholar +1 more source
Scalarization for Multi-Task and Multi-Domain Learning at Scale [PDF]
Neural Information Processing Systems, 2023 Training a single model on multiple input domains and/or output tasks allows for compressing information from multiple sources into a unified backbone hence improves model efficiency.
Amelie Royer +2 more
semanticscholar +1 more source
Type I critical dynamical scalarization and descalarization in Einstein-Maxwell-scalar theory [PDF]
Science China Physics Mechanics and Astronomy, 2023 We investigated the critical dynamical scalarization and descalarization of black holes within the framework of the Einstein-Maxwell-scalar theory featuring higher-order coupling functions.
Jianbo Jiang +6 more
semanticscholar +1 more source
Testing the limits of scalar-Gauss-Bonnet gravity through nonlinear evolutions of spin-induced scalarization [PDF]
Physical Review D, 2023 Quadratic theories of gravity with second order equations of motion provide an interesting model for testing deviations from general relativity in the strong gravity regime.
D. Doneva +4 more
semanticscholar +1 more source
A framework for controllable Pareto front learning with completed scalarization functions and its applications [PDF]
Neural Networks, 2023 Pareto Front Learning (PFL) was recently introduced as an efficient method for approximating the entire Pareto front, the set of all optimal solutions to a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping between a preference
T. Tran, Long Hoang, Dung D. Le, T. Tran
semanticscholar +1 more source
Effects of mass and self-interaction on nonlinear scalarization of scalar-Gauss-Bonnet black holes [PDF]
Physical Review D, 2023 It was recently found that in certain flavours of scalar-Gauss-Bonnet gravity linearly stable bald black holes can co-exist with stable scalarized solutions.
A. M. Pombo, D. Doneva
semanticscholar +1 more source