Results 231 to 240 of about 2,019,981 (302)

Device Stochasticity Directly Implements Bayesian Sampling in Spiking Neurons

open access: yesAdvanced Science, EarlyView.
Brain‐inspired stochastic spiking turns device noise into Bayesian uncertainty for robust, low‐power neuromorphic computing. ABSTRACT Device stochasticity is considered a non‐ideality in neuromorphic hardware, although stochastic neural activity supports probabilistic computation in biological systems.
Huannan Zheng, Jingli Liu, Kezhou Yang
wiley   +1 more source

Steric‐Pressure‐Driven Topological Reconstruction for High‐Performance Zintl Phase Thermoelectrics with Machine‐Learning Validation

open access: yesAdvanced Science, EarlyView.
Synergistic Sr and Ag co‐doping drives steric‐pressure‐induced reconstruction from a 2D layered structure to a cross‐linked 3D framework in the Ca9‐xSrxZn4+y‐zAgzSb9 system, enhancing carrier mobility while maintaining a large Seebeck coefficient and doubling ZT to 0.73 at 873 K, with hybrid transfer machine learning further capturing the compositional
Siyeon Kim   +7 more
wiley   +1 more source

Accelerating High‐Fidelity Airfoil Design via Physics‐Informed Video Diffusion Model

open access: yesAdvanced Science, EarlyView.
Physics‐informed video diffusion enables rapid inverse aerodynamic design by mapping target lift‐to‐drag response to airfoil geometries and full‐field flow distributions. Trained on 53 400 DNS samples, the model achieves over 90% fidelity with ∼30 s inference, while experimentally realized propeller designs demonstrate a practical pathway from ...
Yifeng Ai   +9 more
wiley   +1 more source

ReLink: Reliable Deep Learning for Microscopic Motion Tracking

open access: yesAdvanced Science, EarlyView.
ReLink enables reliable trajectory linking in single‐particle tracking by integrating physics‐informed motion statistics with confidence‐based adaptive filtering. Across diverse simulated and experimental conditions, ReLink robustly reconstructs trajectories and preserves the fidelity of downstream analyses, enabling accurate characterization of ...
Yuan Jiang   +7 more
wiley   +1 more source

A CT Lesion Foundation Model for Non‐Invasive Assessment of Hepatocellular Carcinoma Aggressiveness and Prediction of Recurrence Risk

open access: yesAdvanced Science, EarlyView.
A computed tomography lesion foundation model, built by sequential self‐supervised pretraining on generic and hepatocellular carcinoma‐specific lesion images, non‐invasively assesses microvascular invasion and pathological grade from contrast‐enhanced CT and stratifies postoperative recurrence risk.
Wei Xia   +9 more
wiley   +1 more source

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