Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Bayesian Integration of Tumor Mutational Signatures and Somatic Features Refines Pathogenicity Assessment of Germline Mismatch Repair Variants. [PDF]
Amzaleg Y +6 more
europepmc +1 more source
Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison +4 more
wiley +1 more source
Bayesian Meta-Analysis of Transcatheter Mitral Valve Edge-to-Edge Repair for Secondary Mitral Regurgitation in Heart Failure. [PDF]
Schurr JW +9 more
europepmc +1 more source
This study proposes a novel weighted random forest multimodal fusion method that combines smart glasses and sEMG data for in‐vehicle gesture interaction. It realizes stable performance in dim, occluded, and other constrained scenarios, providing feasible solutions and laying a foundation for universal human–machine interaction.
Wenbo Zhang +8 more
wiley +1 more source
From Inference to Ritual: Why Frequentist Statistics Became Hard to Understand and Teach in Medicine. [PDF]
Lega JC, Chevret S.
europepmc +1 more source
Isotopic reconstruction of the weaning process in the archaeological population of Canímar Abajo, Cuba: A Bayesian probability mixing model approach. [PDF]
Chinique de Armas Y +7 more
europepmc +1 more source
Energy‐Aware Perturbation Optimization for Memristor‐Array Convolutional Neural Networks
Memristor‐array inference becomes more energy efficient when layer inputs are reshaped before computation. Sinusoidal perturbation encoding with dual‐threshold screening reduces active voltage pulses and contracts ADC input‐current ranges, jointly lowering crossbar and peripheral energy while preserving accuracy across hardware MNIST validation, deep ...
Ao Xu +6 more
wiley +1 more source
Microbial Primer: Bayesian learning of traits from microbial time series data. [PDF]
Dey R +9 more
europepmc +1 more source
An AI‐enabled micromixing framework is developed by integrating cGAN with Bayesian optimization for predictive control of microrobot‐driven flow manipulation. Through this framework, the spatiotemporal evolution of micromixing is learned directly from experimental images, while rapid identification of optimized microrobot actuation strategies is ...
Dineshkumar Loganathan, Chia‐Yuan Chen
wiley +1 more source

