AxiomOcean deploys a 3D encoder-backbone-decoder architecture that jointly predicts upper-ocean variables and outperforms prior AI models by 20-35% in day-1 RMSE while preserving eddy kinetic energy and vertical consistency.
Application of machine learning in breast cancer survival prediction using a multimethod approach,
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Targeted halting of gradient flow at unstable material boundaries enables stable derivatives for optimizing detector designs in radiation transport simulations.
Parametrically driven oscillators achieve optimal reservoir computing performance for chaotic time-series prediction in the 2:1 parametric resonance regime, with performance degrading in chaotic frequency-comb states.
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
AlSb in cubic and hexagonal phases shows quasi-direct band gaps of 1.71 eV and 1.50 eV with strong visible-UV absorption and increasing power factor with carrier concentration when computed with mBJ+U.
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.
citing papers explorer
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AxiomOcean: Forecasting the Three-Dimensional Structure of the Upper Ocean
AxiomOcean deploys a 3D encoder-backbone-decoder architecture that jointly predicts upper-ocean variables and outperforms prior AI models by 20-35% in day-1 RMSE while preserving eddy kinetic energy and vertical consistency.
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Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Targeted halting of gradient flow at unstable material boundaries enables stable derivatives for optimizing detector designs in radiation transport simulations.
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Neuromorphic Computing Based on Parametrically-Driven Oscillators and Frequency Combs
Parametrically driven oscillators achieve optimal reservoir computing performance for chaotic time-series prediction in the 2:1 parametric resonance regime, with performance degrading in chaotic frequency-comb states.
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Neuralized Fermionic Tensor Networks for Quantum Many-Body Systems
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
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Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
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Optoelectronic and Thermoelectric Properties of High-Performance AlSb Semiconductors
AlSb in cubic and hexagonal phases shows quasi-direct band gaps of 1.71 eV and 1.50 eV with strong visible-UV absorption and increasing power factor with carrier concentration when computed with mBJ+U.
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Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
Transfer learning from PREDICT v3 and de-novo random survival forests improve calibration of five-year breast cancer survival predictions over the baseline in MA.27 data while handling missing information, with benefits seen in SEER but not TEAM validation.