Results 121 to 130 of about 92,621 (309)
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation
Frontiers in Psychology, 2014 Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood.
Oliver W. Layton +2 more
doaj +1 more source
Single-hidden layer feedforward neural network [14].
, 2018 Single-hidden layer feedforward neural network [14].
Yinggan Tang (5909213) +3 more
core +1 more source
Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network
Advanced Intelligent Discovery, EarlyView.We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang +16 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.In this research, a paradigm of parameter estimation method for pneumatic soft hand control is proposed. The method includes the following: 1) sampling harmonic damping waves, 2) applying pseudo‐rigid body modeling and the logarithmic decrement method, and 3) deriving position and force control.
Haiyun Zhang +4 more
wiley +1 more source
Feedforward equilibrium trajectory optimization with GSPulse
Nuclear FusionOne of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver.
J.T. Wai +7 more
doaj +1 more source
The role of the feedforward paradigm in cognitive psychology
, 2006 Feedforward control is a process adjusting behaviour in a continuative way. Feedforward takes place when an equilibrium state is disrupted and the system has to automatically retrieve the homeostatic stable state.
Marta Olivetti Belardinelli +2 more
core +1 more source
Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training
Advanced Intelligent Systems, EarlyView.The environmental footprint of spiking neural networks is quantified during dataset encoding and training for autonomous driving regression across three benchmarks. Temporal depth emerges as the dominant driver of energy consumption and CO2 emissions, while the accuracy–energy trade‐off proves dataset‐dependent. On conventional hardware, spiking models
Fernando S. Martínez +3 more
wiley +1 more source
Feedforward conductance versus feedforward connectivity.
, 2017 A: Quality of replay as a function of connectivity and synaptic strength. B: The replay as a function of connectivity and total feedforward conductance input shows that the propagation is independent of connectivity as long as the total feed-forward ...
Henning Sprekeler (183138) +2 more
core +1 more source
Benchmarking Data‐Driven Control of Octopus‐Inspired Soft Arms in Underwater Environment
Advanced Intelligent Systems, EarlyView.Underwater soft robots present safe, compliant interaction, yet reproducible control remains scarce. This work presents an open benchmark for octopus‐inspired arms: a smooth, velocity‐diverse data‐collection scheme produces a compact dataset to train a vanilla policy.
Muhammad Sunny Nazeer +6 more
wiley +1 more source