AIMC Topic: Humans

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Biomimetic Neural Intelligent E-Skin System for Tactile Perception and Robotic Decision-Making.

ACS sensors
The widespread application of electronic skin (e-skin) in human-machine interaction necessitates intelligent and information-rich systems. However, the rapid and efficient deployment of e-skin for high-precision multisensor fusion remains a critical ...

Covarying gray and white matter networks characterize schizophrenia and bipolar disorders on a continuum: A data fusion machine learning approach and a brain network analysis.

Journal of affective disorders
Schizophrenia (SZ) and Bipolar disorder (BD) share genetic and cerebral abnormalities, supporting an expanded continuum hypothesis. In this paper, we aim to better characterize differences and commonalities of gray and white matter features between S...

Shapley value-driven multi-modal deep reinforcement learning for complex decision-making.

Neural networks : the official journal of the International Neural Network Society
Deep Reinforcement Learning (DRL) has made significant strides in addressing various sequential decision-making problems, particularly in domains such as game simulations and robotic control. However, substantial challenges arise when DRL is applied ...

Comprehensive disentanglement with fine-grained feature mitigation for domain generalization.

Neural networks : the official journal of the International Neural Network Society
Domain generalization is proposed as an approach capable of solving the domain shift challenge, which aims at generalizing knowledge learned from multiple source domains with different distributions to the target domain that is invisible during the t...

Path-aware multi-scale learning for heterogeneous graph neural network.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous Graph Neural Networks (HGNNs) are a powerful tool for modeling data with diverse node and edge types, found in applications like social networks, recommendation systems, and knowledge graphs, including tasks such as node classification,...

The architecture design and training optimization of spiking neural network with low-latency and high-performance for classification and segmentation.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are the new third generation of bio-mimetic neural networks suitable for large-scale parallel computation due to its advantages of low power consumption and low latency. However, most of the training algorithms and netw...

RIVA: Efficient relational inference with variate attention.

Neural networks : the official journal of the International Neural Network Society
Interactive systems are omnipresent across various domains, ranging from dynamic systems in physics to intricate societal dynamics. Relational inference aims to uncover implicit interactions between components based on observed system trajectories. E...

Unified semantic space learning for cross-modal retrieval.

Neural networks : the official journal of the International Neural Network Society
With the increasing amount of multimodal data on the Internet, cross-modal retrieval has gradually become a hot research topic and has achieved significant progress, especially since graph convolutional networks were introduced. Most methods based on...

Shaping pre-trained language models for task-specific embedding generation via consistency calibration.

Neural networks : the official journal of the International Neural Network Society
Pre-trained language models (PLMs) have shown significant success in various downstream tasks by providing initial parameters for task-specific fine-tuning. An inherent challenge of this approach is that adapting solely to downstream tasks may lead t...

Learning multi-regularized mutation-aware correlation filter for object tracking via an adaptive hybrid model.

Neural networks : the official journal of the International Neural Network Society
Discriminative Correlation Filters (DCF) have emerged as a popular and effective approach in object tracking. With promising performance and efficiency, DCF-based trackers achieved impressive attention and reliable tracking results in several challen...