AI Medical Compendium Topic

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UMS-ODNet: Unified-scale domain adaptation mechanism driven object detection network with multi-scale attention.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation techniques improve the generalization capability and performance of detectors, especially when the source and target domains have different distributions. Compared with two-stage detectors, one-stage detectors (especial...

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

PLoS computational biology
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...

Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.

Cortex; a journal devoted to the study of the nervous system and behavior
Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great poten...

Semantic-guided attention and adaptive gating for document-level relation extraction.

Scientific reports
In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based model...

GradToken: Decoupling tokens with class-aware gradient for visual explanation of Transformer network.

Neural networks : the official journal of the International Neural Network Society
Transformer networks have been widely used in the fields of computer vision, natural language processing, graph-structured data analysis, etc. Subsequently, explanations of Transformer play a key role in helping humans understand and analyze its deci...

DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition.

IEEE transactions on neural networks and learning systems
With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning al...

Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

IEEE transactions on neural networks and learning systems
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...

A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.

Sensors (Basel, Switzerland)
For the current wearable devices in the application of cross-diversified user groups, it is common to face the technical difficulties of static sign language recognition accuracy attenuation, weak anti-noise ability, and insufficient system robustnes...

Personalized multi-head self-attention network for news recommendation.

Neural networks : the official journal of the International Neural Network Society
With the rapid explosion of online news and user population, personalized news recommender systems have proved to be efficient ways of alleviating information overload problems by suggesting information which attracts users in line with their tastes....

Hypergraph contrastive attention networks for hyperedge prediction with negative samples evaluation.

Neural networks : the official journal of the International Neural Network Society
Hyperedge prediction aims to predict common relations among multiple nodes that will occur in the future or remain undiscovered in the current hypergraph. It is traditionally modeled as a classification task, which performs hypergraph feature learnin...