AIMC Topic: Neural Networks, Computer

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Deep reinforcement learning can promote sustainable human behaviour in a common-pool resource problem.

Nature communications
A canonical social dilemma arises when resources are allocated to people, who can either reciprocate with interest or keep the proceeds. The right resource allocation mechanisms can encourage levels of reciprocation that sustain the commons. Here, in...

Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks.

NAR genomics and bioinformatics
The spatial conformation of chromosomes and genomes of single cells is relevant to cellular function and useful for elucidating the mechanism underlying gene expression and genome methylation. The chromosomal contacts (i.e. chromosomal regions in spa...

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.

Proteins
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about prote...

Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems.

Neural networks : the official journal of the International Neural Network Society
Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilf...

Intra-class progressive and adaptive self-distillation.

Neural networks : the official journal of the International Neural Network Society
In recent years, knowledge distillation (KD) has become widely used in compressing models, training compact and efficient students to reduce computational load and training time due to the increasing parameters in deep neural networks. To minimize tr...

AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching.

Neural networks : the official journal of the International Neural Network Society
Local feature matching, which seeks to establish correspondences between two images, serves as a fundamental component in numerous computer vision applications, such as camera tracking and 3D mapping. Recently, Transformer has demonstrated remarkable...

Predicting phototaxis of almond moth, Cadra cautella (Walker) using ANN models: insights for wavelength and intensity as key factors.

Pest management science
BACKGROUND: The almond moth, Cadra cautella (Walker), is a significant pest of stored products globally, causing severe damage and contamination. This insect was reported to have attraction towards light and this phenomenon can be exploited for its m...

Deep Guess acceleration for explainable image reconstruction in sparse-view CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the traditional Filtered Back Projection algorithm suffer from severe artifacts due to sparse data. In c...

Deformable image registration with strategic integration pyramid framework for brain MRI.

Magnetic resonance imaging
Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registration is commonly used in clinical practice for accurate diagnosis and treatment planning. In recent yea...

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diagnosis of rare neuromuscular diseases often relies on muscle biopsy analysis, which varies based on the evaluator's experience. Advances in deep learning show promise in improving diagnostic accuracy by identifying standa...