AIMC Topic: Neural Networks, Computer

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Dynamic intelligent measurement of multiple chirped signals of different types based on the optical computing STFT and the YOLOv3 neural network.

Optics letters
We propose a simultaneous measurement system for multiple signals of different types which combines the optical computing short-time Fourier transform (STFT) and You Only Look Once (YOLOv3) neural network. Through the system, the analytical expressio...

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected ph...

BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task.

Bioinformatics (Oxford, England)
MOTIVATION: Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC sy...

Multi-model fusion short-term power load forecasting based on improved WOA optimization.

Mathematical biosciences and engineering : MBE
The high accuracy of short-term power load forecasting has a pivotal role in helping power companies to construct reasonable production scheduling plans and avoid resource waste. In this paper, a multi-model short-term power load prediction method ba...

Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning.

The Journal of chemical physics
We propose a scheme for ab initio configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of relaxed structures ...

Single-pixel imaging for edge images using deep neural networks.

Applied optics
Edge images are often used in computer vision, cellular morphology, and surveillance cameras, and are sufficient to identify the type of object. Single-pixel imaging (SPI) is a promising technique for wide-wavelength, low-light-level measurements. Co...

Feature recognition of a 2D array vortex interferogram using a convolutional neural network.

Applied optics
A vortex array has important applications in scenarios where multiple vortex elements with the same or different topological charges are required simultaneously. Therefore, the detection of the vortex array is vital. Here, the interferogram between t...

Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations.

Mathematical biosciences and engineering : MBE
Physics-informed neural networks (PINN) have lately become a research hotspot in the interdisciplinary field of machine learning and computational mathematics thanks to the flexibility in tackling forward and inverse problems. In this work, we explor...

Can deep convolutional neural networks support relational reasoning in the same-different task?

Journal of vision
Same-different visual reasoning is a basic skill central to abstract combinatorial thought. This fact has lead neural networks researchers to test same-different classification on deep convolutional neural networks (DCNNs), which has resulted in a co...

IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consu...