AIMC Topic: Computer Simulation

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Simulations meet machine learning in structural biology.

Current opinion in structural biology
Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of lo...

Maternal exposure to ambient PM during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models.

The Science of the total environment
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM) on CHDs are inconsistent. We used ...

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering.

Biological cybernetics
A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesti...

Classifying vortex wakes using neural networks.

Bioinspiration & biomimetics
Unsteady flows contain information about the objects creating them. Aquatic organisms offer intriguing paradigms for extracting flow information using local sensory measurements. In contrast, classical methods for flow analysis require global knowled...

Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a...

full-FORCE: A target-based method for training recurrent networks.

PloS one
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input...

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

International journal of medical informatics
Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular...

Bipedal robotic walking control derived from analysis of human locomotion.

Biological cybernetics
This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ...

Neural network for nonsmooth pseudoconvex optimization with general convex constraints.

Neural networks : the official journal of the International Neural Network Society
In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which doe...